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Monday, June 29, 2026

No, Socialism Won’t Kill the Democratic Party

The democratic socialists are fighting the battles the Democratic Party have refused to wage. This is the way.

New York Mayoral Candidate Zohran Mamdani, center, celebrates with Sen. Bernie Sanders (I-VT), left, and U.S. Rep. Alexandria Ocasio-Cortez (D-NY), right, during an election rally on October 26, 2025 at Forest Hills Stadium in the Queens borough of New York City. The mayoral election will take place on November 4, 2025.
(Photo by Andres Kudacki/Getty Images)

Corbin Trent
Jun 28, 2026

Common Dreams

Maybe you’ve heard the phrase means of production and maybe you haven’t. It basically means the tools, land, factories, machines, infrastructure, and systems a society uses to make the material stuff of life. Who owns those means, who controls them, and who benefits from them is one of the oldest fights in politics.

The communists, at their extreme, think the state should own and control all of it. The capitalists, at their extreme, think it should be completely in private hands. Socialists like me think there ought to be a blend of public and private ownership, that capitalism and socialism work best when paired. The neoliberals, which is mostly what we’ve got now, think our future should be in private control but paid for by the people, maybe with a guardrail or two set up between the people and the private sector’s insatiable desire for profit.

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We fought about this hard in the early 1900s. There was a big movement around labor and organizers and workers, and a lot of those folks were actual communists. The communists were fighting for the means of production and the capitalists were fighting for it too, and they fought tooth and nail. The workers were unionizing and fighting for better rights and better conditions, and these were actual fights, with guns and sticks and knives, and people got killed, mostly workers. They fought for more rights. They fought for the 40-hour week. They fought for overtime. They fought for working conditions that were safe and not deadly, and in a lot of cases they won. They won those fights with blood. They won those fights with effort. They won those fights by putting things on the line.

You might have heard of something called the weekend. Not the singer, though I love him. The idea that Monday through Friday is the work week and the weekend is for your life. You might have heard of the eight-hour day, that anything over eight hours is overtime. Both of those were brought to you by the labor movement, a labor movement that at one point was empowered not to fight for the members of its own labor union but empowered to fight for people who worked for a living. That was their mantra. That was their goal.

Then came the New Deal in the 30s, the people injecting themselves into the production of the things we needed to rebuild the country after the Great Depression. We did it through the Civilian Conservation Corps. We trained workers, we provided health care, and during the war we even created daycare centers so women could go into the factories. It was a real rebalancing of our economy between capital and labor, with the state taking part of the means of production, engineers and scientists doing the work for the people, paid for by the people, and then used by the people. Corporations got brought to heel for a while.

Then we beat the fascists, the Nazis in Germany and Italy and the imperialists in Japan, and right after that the Americans decided the biggest scourge, the biggest fear they had, was communism and socialism. Because we’d gotten a taste through the New Deal and the Arsenal of Democracy of what it was like to share in the growth, to share in the fruits of our own labor, and there was a fear that if we kept tasting it we’d decide we too deserved more, and that would mean the Vanderbilts and the railroad tycoons and the shipping barons and the oilmen would have less and the people who did the work would have more. So we fought it. We fought it through the McCarthy era, with propaganda, with all sorts of ideological battles. The idea of socialism and the idea of communism both lost. And the Democratic Party started moving away from its socialist roots and its socialist ideas toward what would ultimately become neoliberalism, the system we’ve got now.

We went through all of those fights, the prisons, the violations of the Constitution. We perverted ourselves in order to fight off socialism, to keep the means of production in the hands of the capitalists, because they alone were able to properly guide our system. And then what did they do with our productive capacity? What did they do with it through the 70s and 80s and 90s and 2000s and right up to today? They shipped it off to Mexico and China and Brazil. They gave away the very thing we fought over. And why? Because it was cheaper, more profitable, and they figured they could do it with impunity.

But when you take away people’s means of production, you also take away their means of making a living, their power and their value in life, economically and socially and every other way, and then you’ve got people fighting over what little is left, and it turns ugly and it turns dirty. Look at January 6th. Look at the riots and the protests during the Black Lives Matter movement. What you end up with is a police force that has to oppress, and private prisons that have to fill up, and a military-industrial complex that doesn’t care whether it’s participating in a genocide or not, because it’s about money and power. And ultimately what you end up with is a country that can’t defend itself or provide for itself, a giant welfare state leaning on the Chinese to make our goods and to buy our debt. A nation that no longer holds its own means of production, no longer holds its own means of making a living, no longer holds its independence, not in energy production, not in the ability to build housing or infrastructure or the things that make our lives better. We import all of it, because all we need is money, we can just make more money.

And that only works as long as the money stays in the hands of a few. All that money creation, all that expansion of wealth, would lead to massive inflation if it weren’t held by a few, and you can already see what it does, because it’s caused massive inflation in indexes and in asset prices. Bitcoin and Apple and the stock market have risen to unreasonable heights, heights that are detached from any reality. Tesla is worth more than the next 30 car companies combined, even though it doesn’t produce as much as any of the top ten and doesn’t make more profit than any of the top ten, and yet somehow it’s worth more. Why? Because the money that’s been created has caused that inflation, and the inflation stays at the top. It makes trillionaires and centibillionaires. If that same money had been shared with the rest of America without creating more productive capacity, without the ability to build more housing or train more doctors or build more hospitals, it would create massive inflation everywhere, because you’d have more money chasing fewer goods in a system that can’t produce the things anymore and just imports them. The inflation is real. It just stays at the top, in asset prices, instead of showing up at the grocery store.

So the means of production was a fight that working people lost and the capitalists won. And then the winners gave away the spoils of their own victory to other nations, because they aren’t patriots and they aren’t citizens of this country. They’re citizens of the world. They’re detached and untethered, private jets and private islands and private security forces, and at that level of wealth they don’t need this country to succeed.

But here’s the thing. The elite, for now, do need us more than we need them. We’re the ones propping them up right now.

The question with AI and robotics is not whether the machines will be powerful. They will be. The question is whether they become another offshore factory, another private island, another asset owned by people who do not need us, or whether they become part of a shared American capacity again.

We lost the last fight over the means of production, and then the winners gave it away. We should not let them do it twice.

Our work is licensed under Creative Commons (CC BY-NC-ND 3.0). Feel free to republish and share widely.

Corbin Trent
Corbin Trent is an Appalachian-born general contractor and political organizer. He co-founded Brand New Congress and Justice Democrats, helped recruit AOC, and served as her first communications director. He publishes AmericasUndoing.com, a project exposing America’s economic decline and calling for bold, public-led rebuilding. Find morework on his TikTok, YouTube, and Facebook channels.
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Algorithms of control and the politics of digital liberation

By Rezgar Akrawi

Published 29 June, 2026

Never before in human history has a small minority wielded this degree of control over the majority, and with such speed and silence. No armies march in the streets, no laws are proclaimed in parliaments; instead, algorithms operate in the shadows, shaping your consciousness, work, opportunities and future. Artificial intelligence (AI) is the most powerful tool humanity has ever produced. The question is not whether it will change the world; it already has. The question is in whose interest will it change it?

Since these ideas were first put forward in my book, Capitalist AI, Challenges for the Left and Possible Alternatives, the pace of AI development has accelerated in a striking and unprecedented manner. Major monopoly-driven corporations in the United States, China and elsewhere have released new models that far surpass their predecessors, now capable of performing tasks once exclusive to humans: medicine, law, programming, creative writing, scientific research and beyond.

Alongside this technological acceleration, generative AI and autonomous intelligent agents have emerged as a qualitatively new development, transforming these systems into independent actors capable of executing chains of decisions without direct human oversight. This relentless acceleration has made it necessary to revisit and further develop my ideas.

The more the capabilities of this technology expand and deepen, the more control over it concentrates in fewer hands, and the wider the gap grows between those who own it and those subjected to it. This equation is not an inevitable fate; it is the product of political and economic choices that can be changed. The trade war over chips and AI has revealed a plain truth: this technology has become a first-order geopolitical weapon, with great powers treating it as an instrument of domination and control. Perhaps the clearest evidence of this is the accelerating military deployment of AI in identifying human targets, conducting combat operations and making life-and-death decisions, in flagrant violation of international humanitarian law.

For the first time in history, it has become genuinely possible to produce what the majority needs with minimal human effort, and provide goods, services and knowledge in abundance without intensive wage labour or traditional bureaucratic structures. Yet these possibilities are constrained and redirected to boost corporate profit, cut wages and deepen class domination.

The proliferation of digital applications, widespread automation and dismantling of market intermediaries in certain sectors all show society possesses tools that could allow for a horizontal, participatory and community-based reorganisation of the economy. Yet this transformation remains shackled by the monopolistic structure that controls technology and directs it toward profit maximisation.

This places a historic responsibility on emancipatory and social-justice forces. They have no choice but to enter the battle over AI and technology with a clear project and real capabilities. The question of who directs AI, and in whose interest, is not an abstract philosophical question — it will determine the shape of the world inherited by the sons and daughters of coming generations.

The gap between left-wing forces and the empire of digital capitalism resembles the gulf between an ant and a huge elephant: on one side, left-wing and social-justice organisations largely lacking financial resources, technical infrastructure and specialised expertise; on the other, monopoly-driven corporate states possessing full control over the digital sphere, data centres spanning continents and armies of engineers and researchers.

Yet this enormous disparity does not mean the battle is decided. When the ant organises well and knows where to strike, it is capable of unsettling the elephant and altering the struggle’s course. Proceeding from this reality, the battle requires clear policies, tactics and tangible tools. The following sections sketch the contours of a left-wing vision, addressing the most important fronts of this struggle.

Developing progressive AI systems

What is possible now

Developing neutral, democratic and open-source systems is fundamental for countering state and corporate dominance over AI. These systems must be managed independently and kept as far as possible from the interests of monopoly capital, to ensure they serve the public rather than private power.

Open-source systems give the public and emancipatory forces an opportunity to participate in developing technology in ways that reflect their values. Any individual or group may freely access the source code, understand it, and modify or improve it. This approach strengthens collective ownership and innovation, and partially dismantles the grip of monopoly-driven corporations. Openness to public scrutiny also reduces the risks of hidden manipulation and ideological steering, making these systems more trustworthy and independent from narrow corporate interests.

Recent years have seen notable developments in this direction. Open-source communities have proven capable to build advanced AI models that compete with what market-ruling corporations produce. Recent experiments have shown building advanced models does not require enormous budgets, opening the door to building socially-oriented models with more modest resources.

Global left-wing and social-justice forces need to support open-source AI projects, adopt them, and direct them toward emancipatory goals — something that remains largely absent from the official programs of most left parties and movements.

Second: what is required in the long term

Left-wing and social-justice forces must globally coordinate to develop and put forward emancipatory alternatives and transparent applications of AI. The goal is guaranteeing technology becomes collective property subject to full public oversight, and oriented toward respect for human rights, equality, social justice and intellectual pluralism.

Rather than remaining the exclusive preserve of wealthy states and large monopoly-driven corporations, AI must become a tool for the majority. One that contributes to solving global and local problems: combating poverty, exploitation and class inequality; achieving equality and advancing democracy; confronting climate change; and developing more inclusive and equitable educational and health systems. In this way, AI is transformed into a global emancipatory project that redefines the relationship between humanity and technology, opening the space for a new model that places technology in the service of people.

AI in the service of manual and intellectual workers

AI, if directed in a socialist and social-justice-oriented manner, can be a powerful tool for human liberation and social justice. It can analyse complex social problems and offer effective solutions to reduce economic disparities and class injustice. Achieving this goal is not automatic; it requires directing its mechanisms and capabilities toward addressing the roots of poverty, unemployment, lack of basic services and social discrimination. Advanced data analysis can also monitor social inequalities, identifying the most deprived communities and formulating equitable policies to address structural imbalances in wealth and services distribution.

Yet documented studies prove that hiring algorithms developed by large corporate players reproduce the racial and class biases embedded in the historical data on which they were trained. This discrimination does not mean programmers were consciously racist; it means the logic of exploitation was encoded into the algorithms through data that reflects the reality of societies built on discrimination and class domination. Dismantling this discrimination requires political change and democratic oversight, not technical adjustment.

AI can be a powerful tool for supporting labour organising and trade union struggle. It can help manual and intellectual workers build digitally-enabled unions and solidarity networks, and strengthen their capacity to negotiate with employers and demand their rights. Experiences of tech-powered unions in Latin America and Europe demonstrate that deploying digital tools to coordinate labour struggle multiplies workers’ capacity for rapid, organised collective action. These tools can also expose corporate practices that exploit workers or suppress union organising, and shed light on the policies of authoritarian regimes that refuse to recognise workers’ right to organise and strike.

AI must be a tool for freeing human beings from routine and exhausting labour, while guaranteeing dignified, stable employment with fair wages. In this model, the labour market is transformed into a more just and open space, where gender, racial, religious and age discrimination can be eliminated through evaluation systems grounded in competence and skill, freed from the social biases that reproduce existing class structures.

Liberating science from monopoly

Rather than becoming a tool that weakens human capacities and produces generations excessively dependent on technology, AI can be redirected to become a means of scientific emancipation and creative growth. It should not replace human thinking; it should expand human capabilities, enable access to advanced knowledge and free up time from routine tasks.

Emancipatory open-source AI systems can stimulate critical thinking, both scientifically and creatively, by encouraging users to explore knowledge independently through questions that prompt analysis and inference, rather than passive reception of ready-made answers without scrutiny.

Current developments reveal a glaring contradiction. The systems that have demonstrated a remarkable capacity to accelerate scientific discovery remain the preserve of those who can pay, and governed by the logic of corporate profitability rather than human need. In practice, diseases that do not generate sufficient profit go untreated. Renewable energy research beneficial to humanity is delayed in favour of research that serves corporate interests. This contradiction makes clear why the question of who owns AI cannot be separated from the question of what it discovers.

Digital cooperatives

Cooperative AI projects can be built, drawing on manual and intellectual workers, engineers, researchers and social activists, with the aim of harnessing technology for the common good. Participation must not just mean their presence as end-users of technology designed by others; the aim is to involve them from the outset in defining the problem, setting priorities and shaping the solution.

Factory workers know from daily experience which tasks drain their physical energy without adding real value. Nurses know which administrative burdens steal their time from patients. Teachers know which bureaucratic procedures prevent them from devoting themselves to genuine education. This accumulated lived knowledge is design knowledge no less important than technical expertise; ignoring it produces systems that solve phantom problems or serve goals remote from the needs of those they claim to serve. This is evident in the Data Workers Inquiry project, where data workers designed their own algorithms to expose corporate exploitation, and in the Decidim platform, built by citizens and engineers together to manage resources locally.

In a genuine digital cooperative, manual and intellectual workers, trade unions and local communities are the owners of the tool they use, rather than mere subscribers paying fees for access.

Toward community sovereignty over technology

Transparent and democratic community oversight of technology is essential. To achieve it, digital power must be redistributed so that technology becomes community-owned and deployed in its service, rather than wielded as a corporate instrument. This requires building participatory institutions and platforms that allow the public to examine how algorithms are designed and applied. It also requires establishing elected popular oversight bodies, at both the local and international levels, with broad representation encompassing workers, academics, human rights advocates and technical experts, to ensure fairness and accountability in the development and operation of AI systems.

Despite the relative value of European legislation in the field of AI, its impact is limited because it operates within the same market logic that produced the problems, frequently resulting in the legal entrenchment of monopoly domination rather than its genuine dismantling. What people need goes beyond these steps, toward genuine community oversight endowed with real authority.

Laws must be enacted and binding guidelines issued that compel developers to embed values of justice and equality at the design stage, with community review imposed on all systems prior to their release. Oversight bodies must be granted genuine powers to review algorithms on an ongoing basis, monitor any embedded biases that could lead to discrimination or exploitation, and retain the capacity to intervene and impose binding regulatory standards.

From the logic of profit to the logic of need

When fierce competition between monopolistic corporations and major powers intensifies in the AI race, the heaviest price will not be paid by shareholders or executive directors. It will be paid by the millions of workers whose jobs automation destroys. This is not speculation; its has already begun across numerous sectors. The urgent need arises to rethink the entire logic of production and distribution from the ground up.

Reorganising production and distribution is a fundamental pillar of the left’s vision for AI. This technology can be used to build systems of democratic collective planning grounded in reliable data and oriented toward social need, allowing resources to be directed efficiently toward society’s requirements. These systems rely on careful analysis of demand and consumption, producing necessary goods and services according to real needs, while avoiding the chronic overproduction that characterises the capitalist system.

Recent global supply chain crises have exposed the fragility of globalised corporate production and its dependence on speculation and monopoly, opening a real discussion on the need for alternative planning models grounded in reliable, democratically governed information. AI can play a decisive role in restructuring supply chains, reducing waste, directing production toward underserved regions and enhancing environmental sustainability. Intelligent logistics systems can also enable more efficient distribution of goods and services and identify optimal routes for reducing carbon emissions.

Moreover, AI can bring about a radical transformation in socially-oriented cooperative production, enabling cooperatives and community enterprises to benefit from intelligent technologies to improve operational efficiency, reduce costs and ensure equitable resource distribution among members. Technology can serve as a tool for building a solidarity economy, helping poor communities achieve economic independence through shared production and equitable distribution of available resources, freed from the grip of monopoly capital.

Dismantling algorithmic patriarchy

Left-wing and social-justice forces must struggle for AI systems that uphold gender justice and contribute to achieving full equality. In 2024, a comprehensive academic study testing AI systems in résumé screening across nine different professions found these systems favoured women’s names in only 11% of cases.

This algorithmic bias is not an isolated technical error. It is a reflection of women’s absence from the design and development process, and an expression of the male-dominant logic encoded into the technology industry, where women still constitute less than 15% of AI researchers at the world’s leading technology companies. UN Women has described how AI systems learn from data saturated with stereotypes, reproducing gender biases and restricting opportunities, particularly in employment, credit and judicial decisions. UNESCO documented alarming evidence in its 2024 study of the prevalence of regressive male gender stereotypes in generative AI. A graver challenge lies in the endeavours of certain authoritarian states with patriarchal religious systems to build their own AI frameworks, with the aim of entrenching male-dominated value systems and consolidating control over women through more sophisticated and harder-to-resist tools.

These systems deliberately encode gender discrimination into their core objectives, from social surveillance to restricting women’s digital presence and imposing behavioural standards derived from conservative religious interpretations. Confronting this requires left-wing, social-justice and feminist forces to wage struggles on two fronts: challenging the algorithmic bias of monopoly-driven corporations and opposing projects of religious patriarchal automation that seek to convert technology into an instrument for reproducing male power under the cover of national sovereignty and cultural relativism.

Algorithms must be trained using comprehensive and diverse data that fully reflects women’s experiences and roles beyond stereotypes. Governments must be pressured to adopt legislation compelling companies to prioritise gender diversity in their technical teams. Masculine-coded language must be removed from AI systems and gender-neutral language developed to help undermine structural discrimination.

Halting AI’s militarisation

The military sector concentrates the largest investment in AI globally. Autonomous weapons capable of deciding to kill without human intervention have become a reality. AI is deployed today to identify human targets and conduct combat operations across many regions, amid an accelerating retreat of human oversight over these decisions. This is not an abstract ethical question; it is fundamentally a class question. Whoever owns these weapons possesses an unprecedented capacity to subjugate populations and suppress resistance.

Every effort must be made to redirect AI toward promoting world peace. Left-wing and social-justice movements can lead global initiatives to pressure governments and international institutions to enact strict legislation preventing AI development for military purposes. AI should equally be employed to document war crimes and human rights violations, contributing to the accountability of authoritarian regimes, states and corporations that seek to militarise technology. Making the public an active party in the struggle against technology’s militarisation means building a global resistance movement capable of ending this inhumane use of technology.

AI and digital repression

AI is being deployed to erode democracy rather than strengthen it, through algorithmic manipulation techniques that feed extremism and deepen political polarisation for commercial and political purposes, and through forgery and disinformation tools that have become more sophisticated and harder to detect.

Current developments reveal a disturbing and accelerating expansion in the deployment of these technologies within the repressive apparatus of authoritarian regimes. Facial recognition systems are used extensively to monitor protests and political gatherings. The automated analysis of digital content has become a systematic tool for targeting activists, dissidents and journalists. Amnesty International has documented how certain states weaponise social media and digital tools to suppress youth protests.

This digital repression takes multiple and increasingly dangerous forms. At one end lies systematic digital demoralisation, fed by algorithms designed to spread a sense of powerlessness and futility. Further along comes digital arrest, through account restrictions and deletions on spurious grounds. At the extreme end sits digital assassination, by completely erasing dissidents’ online existence. There is also voluntary self-censorship, where activists impose restrictions on themselves out of fear of bans or account closures. Human Rights Watch has documented numerous cases in which technologies sold by Western companies were used to track and arrest dissidents, making these companies partners in human rights violations.

A struggle must be waged to establish strict international and domestic legal frameworks criminalising AI’s use to manipulate public opinion and violate human rights. We also need global solidarity networks to monitor violations, the boycott and blacklisting of companies that sell surveillance technologies to authoritarian regimes, and the development of data encryption and communications security technologies to protect activists and dissidents.

AI and environmental collapse

There is a profound contradiction that demands frank confrontation. Training large AI models consumes enormous quantities of energy and water and their operations produce carbon emissions equivalent to millions of flights annually. Reliable reports reveal some major data centres consume enough water to supply entire cities. AI, in its current corporate form, does not help solve the environmental crisis; it deepens it, even as companies claim to deploy it for sustainability. Any serious emancipatory alternative must place this contradiction as a core priority.

AI must contribute to environmental economic planning, with its analytical capabilities deployed to regulate production according to society’s actual needs. Socially-oriented management models can achieve more efficient resource use, reduced waste and technological development directed toward transformative environmental solutions, such as improving renewable energy systems and sustainable water management.

AI’s use in projects that destroy the environment must be prohibited. The licensing of any AI technology must be linked to an assessment of its environmental impact. Alongside this ,we need intelligent systems to monitor corporate compliance with environmental standards. This requires developing systems that reduce excessive energy consumption and advance reliance on renewable energy. Within an emancipatory framework, this technology can be redirected to become an effective instrument for protecting natural resources and building an economy to serve society and the planet together.

Toward a militant digital left international

AI is a mirror of the society that produces it, accurately reflecting the power relations that control its direction, funding and priorities. When monopoly-driven corporations, great powers and authoritarian states are the ones building and financing this technology, they build it according to their own logic: the logic of maximising profits, deepening class domination and political control, and reproducing more sophisticated forms of exploitation and repression that are harder to resist. It is no coincidence that the military and security sector is the largest investor in AI development globally; it is an explicit expression of capitalism’s true priorities in its digital phase.

Yet this mirror is not an inevitable fate. Either AI continues as an instrument of class domination in the hands of a minority that uses it to control production, distribution, consciousness and politics, or we make it emancipatory collective property that liberates the majority from the burdens of exploitation. Achieving this is neither easy nor close at hand, given the enormous current imbalance between the left and digital capitalism.

This is a long-term, cumulative project requiring vast human, technical and organisational energies, as well as time, perseverance and the capacity to endure setbacks and move beyond them. The difficulty of the task, however, does not imply its impossibility. The left’s history is replete with struggles that appeared impossible and concluded in radical transformations.

The digital left that simultaneously masters work in the field and excels in deploying digital space is the left best positioned to confront capitalism in its digital phase. At the global level, this means building a digital left international capable of confronting the planetary hegemony of digital capitalism with its own tools and in the language of its era, bringing together left-wing and social-justice forces around the world in a shared project that places technology in the service of the masses, emancipation, justice and equality.

What I have outlined is not a romantic dream; it is a real political battle unfolding now. Every day that passes without left-wing and social-justice forces engaging it with awareness and organisation is a day in which the structures of digital domination become more entrenched and harder to dismantle. Technology has never been neutral, but today it is less neutral than ever. What we build today — however small it may appear — is the seed of the future we want.

Rezgar Akrawi is an independent leftist who is interested in the left and the technological revolution, and works as an expert in system development and e-governance. He is coordinator of the Center for Marxist and Leftist Studies and Research.

This work is licensed under CC-BY-NC-ND-4.0

Sunday, June 28, 2026

Promising single-dose malaria treatment advances toward pan-African clinical trial

DZIF project aims to overcome major barriers to malaria control. Study principal investigator Prof. Ghyslain Mombo-Ngoma named to TIME100 Health 2026.

German Center for Infection Research

Antimalarial four-drug combination study in Gabon — image:
Treatment of a patient as part of the clinical SPAP study in Gabon. Pictured are nurse Merleye Nongou (standing), data clerk Naomie Badinga (seated), and physician Dr. Alex Hounmenou Zinsou. The photo was taken at the satellite site of the Centre de Recherches Médicales de Lambaréné (CERMEL) in Mighoma, Tchibanga.
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Credit: Ghyslain Mombo-Ngoma/BNITM/CERMEL

Researchers at the German Center for Infection Research (DZIF) have developed a promising single-dose malaria treatment that could help address growing drug resistance and simplify treatment for patients. The four-drug combination treatment, known as SPAP, is now being prepared for large-scale clinical testing across Africa. Prof. Ghyslain Mombo-Ngoma, who was named to the 2026 TIME100 Health list in recognition of his contributions to global health research, is co-leading the project.

Despite major progress in recent decades, malaria continues to cause hundreds of thousands of deaths every year, primarily in sub-Saharan Africa. Drug resistance is increasing, and many patients struggle to complete treatment regimens that require medication over several days. While new antimalarial drugs are being developed, it may take years before they become widely available.

To address these challenges, researchers supported by DZIF are investigating how to combine existing medicines more effectively. They have developed SPAP, a new single-dose combination therapy based on the four already approved antimalarial drugs, sulfadoxine, pyrimethamine, artesunate and pyronaridine.

Results from a clinical trial conducted in Gabon suggest that SPAP could significantly improve malaria treatment. By targeting the malaria parasite through multiple mechanisms, SPAP has the potential to overcome two major barriers to malaria control: increasing drug resistance and poor adherence to multi-day treatment schedules.

"Efforts are underway to develop next-generation antimalarial medicines, but it will take many years for them to reach the market," explains Prof. Peter Kremsner, a world-renowned malaria researcher at University Hospital Tübingen, regarding the objective of the study. "It is paramount to establish a regimen for an optimal use of combinations of existing medicines to cover this period."

Next step: a pan-African clinical trial

To validate the promising findings of the previous study, the researchers are planning a large, multi-country clinical trial in Africa to evaluate the safety and effectiveness of SPAP under real-world conditions. The potential of SPAP has also been recognized by the World Health Organization (WHO), which has included the therapy on its list of priority malaria medicines under development. Production of fixed-dose SPAP tablets for the pan-African clinical study is expected to begin this year. If the clinical trial confirms these results, SPAP could be a significant advancement in malaria treatment in sub-Saharan Africa, offering a simpler, more effective therapy.

"This study addresses one of the most pressing challenges in malaria treatment: maintaining effectiveness while reducing the risk of resistance development," says co-project leader Prof. Ghyslain Mombo-Ngoma of the Bernhard Nocht Institute for Tropical Medicine (BNITM). "A single-dose regimen could considerably simplify treatment and improve access for patients in endemic regions," Mombo-Ngoma, who is also a group leader at the Centre de Recherches Médicales de Lambaréné (CERMEL) in Gabon, adds. CERMEL is one of four African Partner Institutions with which DZIF scientists already have long-standing collaborations.

"The strength of this project lies in the close collaboration between research institutions across Africa and Europe," says Dr. Oumou Maïga Ascofaré, a group leader at BNITM and the Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR) in Ghana and the third principal investigator (PI) behind the pan-African clinical study. "Together, we aim to generate evidence that can support future malaria treatment strategies where they are needed most."

The pan-African clinical trial is receiving significant support from the DZIF. All three principal investigators are scientists in the DZIF research area Malaria and Neglected Tropical Diseases.

Ghyslain Mombo-Ngoma named to TIME100 Health 2026

The international visibility of this work is reflected in a recent honor for one of its lead investigators. Prof. Mombo-Ngoma was named to the TIME100 Health 2026 list, which recognizes the 100 most influential individuals shaping the future of health worldwide.

Through this award, TIME not only highlights individual leadership, but also emphasizes the critical importance of translating biomedical research into sustainable health improvements for populations worldwide. This recognition underscores the value of international research partnerships in addressing some of the world's most pressing health challenges.

"I am deeply honored to be included in the TIME100 Health list," says Prof. Mombo-Ngoma. "This recognition reflects the efforts of many colleagues and partners in Africa and Europe who are committed to reducing the burden of neglected infectious diseases. Our work is driven by the belief that scientific excellence must translate into real health benefits for communities that need it most."

Prof. Mombo-Ngoma is an internationally recognized expert in clinical and implementation research on poverty-related infectious diseases. He leads the Drug Implementation Research Group at BNITM in Hamburg and the Medicines for Poverty-Related Infectious Diseases and Implementation Research Group at CERMEL, an African partner institution of DZIF, and holds a joint professorship with the University Medical Center Hamburg-Eppendorf (UKE). His research focuses on developing and evaluating new treatments for malaria, schistosomiasis, and other infectious diseases that disproportionately affect women, children, and adolescents in sub-Saharan Africa.

In addition to leading multinational clinical trials, Prof. Mombo-Ngoma is committed to strengthening research capacity in Africa and improving maternal and child health through evidence-based interventions. His work unites research institutions across Africa and Europe to accelerate the development and implementation of new health solutions.

The complete TIME100 Health 2026 list and a portrait of Prof. Mombo-Ngoma can be found under the respective links.

Source: Bernhard Nocht Institute for Tropical Medicine (BNITM) press release on the TIME100 Health 2026 award.

Journal

Malaria Journal

DOI

10.1186/s12936-025-05559-4

Method of Research

Randomized controlled/clinical trial

Subject of Research

People

Authenticity, regulation, and authority in the age of AI

JMIR Publications

(Toronto, June 26, 2026) JMIR Publications released three News and Perspectives stories exploring different areas of authenticity in the current clinical and scholarly landscape: regulation tensions around AI chatbots presenting themselves as medical authorities; the danger of AI-generated deepfakes of real physicians, and the American Medical Association’s response; and the rising issue of academic identity theft.

The Rise of Unlicensed Medical AI

“Regulating AI Chatbot Impersonations of Medical Professionals”, authored by MD-PhD candidate Tejas S Athni, covers recent controversies—and growing regulatory concern—involving conversational medical AI chatbots. One such controversy is the May 2026 enforcement action in Pennsylvania against Character.AI, which involved a chatbot falsely representing itself as a licensed psychiatrist, exercising the authority to assess a patient's medication needs.

These AI companies “may benefit from the appearance of medical authoritativeness yet avoid the formal legal obligations of licensed medical practice,” notes Athni, pointing to the a separate controversy involving Doctronic, an AI platform which markets itself as an AI doctor while simultaneously disclaiming that it is not licensed and does not practice medicine. Existing laws and regulations are so far struggling to keep pace. “At present,” writes Athni, “existing medical licensing or consumer protection frameworks remain underdeveloped to address these challenges—the legal landscape is fragmented.”

Please cite as:

Athni TS. Regulating AI Chatbot Impersonations of Medical Professionals.

J Med Internet Res 2026;28:e104835

URL:https://www.jmir.org/2026/1/e104835

doi: 10.2196/104731

The Deepfake Will See You Now

Shalini Kathuria Narang’s “American Medical Association Shares Framework to Address the Escalating Risk of Physician Deepfakes” reports on use of AI technology to create deepfakes—fraudulent recreations—of physicians, and the American Medical Association’s response: a recently released policy framework for protecting digital physician identity.

Speaking with Shannon Curtis of the AMA Center for Digital Health and AI, Narang discusses how these deepfakes can damage “the reputations of those they impersonate and…lead to people making decisions about their health based on fake claims”, and details seven policy principles developed by the AMA to address this issue:

Physician identity as a protected right;
Prohibition on deceptive medical impersonation;
Informed, opt-in, and revocable consent;
Mandatory transparency and labeling;
Shared responsibility to prevent impersonation;
Enforcement and practical remedies;
Minimizing administrative burden.

The next step is to translate this policy framework into actual policy, regulating AI-generated and -altered content depicting physicians through a set of enforceable guidelines. “The AMA is eager to collaborate with lawmakers, regulators, and industry to protect patients and doctors from these risks,” writes Narang.

Please cite as:

Narang SK. American Medical Association Shares Framework to Address the Escalating Risk of Physician Deepfakes.

J Med Internet Res 2026;28:e104953

URL: https://www.jmir.org/2026/1/e104953

doi: 10.2196/104953

AI-Accelerated Academic Identity Theft

Cliff Dominy sheds light on another fraudulent use of AI: false authorship in scientific publishing. In “Identity Theft in Academia: Predatory Journals, AI, and the Rise of False Authorship”, Dominy reports that predatory journals are increasingly attaching the names of prominent scientists onto the bylines of fake research—now often created with generative AI tools—without their knowledge or consent.

This strategy is designed to boost the credibility of predatory journals in order to attract unsuspecting, unscrupulous scientists to their brand. The reputational harm to both authors and unwitting editors has exposed weaknesses in the trust-based academic system. Prepublication screening and initiatives like opensci.id, which aims to centralize academic identities in a verified database, may be key to curbing academic identity theft. Combating this AI-accelerated fraud will require robust verification steps and human oversight—and, argues Dominy, a broader reform of the publish-or-perish academic incentive structures that motivates this fraud.

Please cite as:

Dominy C. Identity Theft in Academia: Predatory Journals, AI, and the Rise of False Authorship.

J Med Internet Res 2026;28:e104934

URL: https://www.jmir.org/2026/1/e104934

doi: 10.2196/104934

About JMIR Publications News and Perspectives

JMIR Publications is a leading open access publisher of digital health research. The News and Perspectives section is the newest addition to its portfolio, established to bring the rigor and integrity of academic publishing to scientific journalism. The section features well-researched, expert-driven content from the Scientific News Editor, Kayleigh-Ann Clegg, PhD, and a network of specialist JMIR Publications Correspondents to keep the digital health community informed, inspired, and ahead of the curve.

About JMIR Publications

JMIR Publications is a leading open access publisher of digital health research and a champion of open science. With a focus on author advocacy and research amplification, JMIR Publications partners with researchers to advance their careers and maximize the impact of their work. As a technology organization with publishing at its core, we provide innovative tools and resources that go beyond traditional publishing, supporting researchers at every step of the dissemination process. Our portfolio features a range of peer-reviewed journals, including the renowned Journal of Medical Internet Research.

To find out more about JMIR Publications, visit jmirpublications.com or connect with them on Bluesky, X, LinkedIn, YouTube, Facebook, and Instagram.

The content of this communication is licensed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, published by JMIR Publications, is properly cited.

Journal

Journal of Medical Internet Research

Method of Research

Commentary/editorial

AI can be an ally in rooting out ransomware threats

University of Cincinnati researcher suggests rethinking ways to catch bad actors

University of Cincinnati

AI can be used to prevent cybersecurity threats linked to ransomware, says University of Cincinnati researcher Nelly Elsayed.

“We are in a hype era of AI,” says Elsayed, associate professor in the UC School of Information Technology. “Some people support it, others fear it, but in general people who design technology are trying to use it for good.”

Elsayed, founder and leader of the Applied Machine Learning and Intelligence Lab at UC, recently published research in the Journal of Information Security and Applications, arguing that Generative AI may be an ally in strengthening ransomware defense.

It can be used to integrate synthetic data generation and behavioral forecasting, stress test systems by checking for adversarial behavior simulation and improve trust of human-AI collaboration in security operation systems.

Cybersecurity analysts and system defenders can use AI to detect new malicious attacks and classify and identify new means of attack from bad actors, according to Elsayed. Simulating with hackers might allow for creating possible attack scenarios and learning to think like attackers to offer more robust tools for defense, she adds.

“It’s a way to generate a combination of possible attacks system defenders might not have considered,” she says.

Elsayed adds a practical example could be a user pasting a suspicious email into a generative AI system and asking about the validity of the email. AI could help screen and catch red flags: a suspicious logo or misspellings.

“AI can become an early warning or screening tool,” she says.

In order for AI to be an important tool, Elsaye says that explainability such as why AI considers an item a phishing email is important in building trust among users.

Elsayed said governance and responsible use of AI is a necessity as it can be considered a “double-edged sword.”

“We need governance, rules, policies and a kind of code of conduct,” she adds. “Defining best practices and specifying how AI should be used in an organization are necessities.

“It is possible to build long-term cybersecurity resilience using AI,” says Elsayed.

AI will never replace humans but it can change our work patterns, notes Elsayed, adding that AI has long been part of daily lives in areas such as medical imaging with early detection of disease like lung cancer and facial recognition in smartphones.

“Generative AI made AI more visible, but AI itself is not new,” says Elsayed.

Journal

Journal of Information Security and Applications

Method of Research

Systematic review

Subject of Research

Not applicable

Article Title

Rethinking ransomware defense in the age of generative AI

Using AI to tackle global superbug crisis

University of Queensland

The development of new antibiotics could be sped up thanks to a new tool that tests the reliability of AI.

University of Queensland researchers designed a framework to address the global threat of antimicrobial resistance, testing whether AI can provide reliable reasoning during antibiotic development.

Dr Abdulmujeeb Onawole, from UQ’s Centre for Superbug Solutions at the Institute for Molecular Bioscience, said drug-resistant bacteria was one of the greatest threats to global health, and there was an urgent need for new antibiotics.

“AI is revolutionising drug development, but scientists struggle to trust its recommendations because the technology often can’t explain its reasoning,” Dr Onawole said.

“We call this the ‘black box’ of AI – where AI provides an answer but there’s no explanation of how it got there – and this is preventing scientists understanding the chemical reasoning behind its predictions.

“This lack of transparency is dangerous during antibiotic development as misleading AI explanations can lead to incorrect decisions and wasted resources in the lab.”

Antimicrobial resistance, including resistance to antibiotics, is threatening healthcare globally by limiting effective treatment options against multidrug-resistant pathogens known as ‘superbugs.’

“This is a high-stakes field and while AI can help us to save lives faster, we want to ensure the humans involved can make an informed decision,” Dr Onawole said.

“Longer term, this could contribute to the faster discovery of new antibiotics to combat drug-resistant superbugs.”

In the study, researchers developed 3 AI models using datasets of chemical compounds previously evaluated against the superbug bacterium Staphylococcus aureus.

The framework was tested on each AI model and examined whether AI could correctly identify important drug structures and interpret ‘activity cliffs’ – scenarios where small chemical changes altered a drug’s effectiveness.

Dr Johannes Zuegg from UQ’s Centre for Superbug Solutions, said the research found all 3 AI models were good at spotting known antibiotic structures but differed significantly in their ability to explain what made a molecule active or inactive in developing antibiotics.

“We have shown our framework can successfully assess if AI systems can provide trustworthy chemical explanations, which is critical to medical chemists in drug development,’’ he said.

“This is an important step towards speeding up the integration of AI into antibiotic research.’’

Read the research published in the Journal of Cheminformatics.

Journal

Journal of Cheminformatics

DOI

10.1186/s13321-026-01200-x

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Using AI to tackle global superbug crisis

Article Publication Date

26-Jun-2026

Parallel AI slashes energy costs and carbon emissions in wind-solar-hydrogen power systems

HEP Data Cooperation Journals

image:
Schematic diagram of the distributed reinforcement learning dispatch framework for wind-solar-hydrogen systems
view more
Credit: HIGHER EDUCATION PRESS

As nations race toward carbon neutrality, the intermittency of wind and solar power poses a major challenge to grid reliability. While hydrogen energy storage systems (HESS) offer a promising buffer for days or even seasons, intelligently coordinating these diverse energy sources in real time remains daunting for traditional methods. To tackle this, a team led by He, L. from Northwestern Polytechnical University, China, developed a distributed deep reinforcement learning dispatch framework.

The framework first condenses year-long electricity demand patterns using PCA-enhanced K-means clustering, preserving over 95% of original information. To capture renewable generation uncertainty, the team employed Dynamic Time Warping (DTW) with DBSCAN to extract representative seasonal scenarios that account for nonlinear timing shifts conventional averaging misses.

At its core, a distributed Deep Deterministic Policy Gradient (DDPG) algorithm deploys multiple parallel “actors” exploring different data segments, with a central learner synchronizing their insights—achieving a 5.5-fold speedup (from over 72 hours to 11.5 hours). The system dispatches thermal power, grid purchases, and hydrogen storage while minimizing coal, carbon, and electricity purchase costs. In simulations, the HESS-integrated framework cut total operational costs by 6% (from $56.96 million to $53.6 million) and proved highly robust under meteorological noise, with costs rising by only 0.35%. This work establishes a scalable blueprint for hydrogen storage as an active participant in future low-carbon grids. The work entitled “An energy-efficient scheduling approach for wind-solar-hydrogen systems based on distributed reinforcement learning” was published on AI Agent (published on May 29, 2026).

DOI

10.20517/aiagent.2026.01

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

An energy-efficient scheduling approach for wind-solar-hydrogen systems based on distributed reinforcement learning

New research shows how AI may help avoid unnecessary chemotherapy for breast cancer patients

RCSI

Tuesday, 23 June 2026: Research led by RCSI University of Medicine and Health Sciences and University College Dublin (UCD) has identified immune markers that could help doctors more accurately determine which breast cancer patients are unlikely to benefit from chemotherapy, potentially sparing some patients from unnecessary treatment.

Chemotherapy is regularly used in the treatment of early-stage, ER+HER2- breast cancer, which accounts for around 70% of all breast cancer diagnoses annually. While it remains an important treatment, the side effects can be debilitating and for a significant proportion of patients, the benefit remains uncertain, raising concerns about overtreatment for those who may have remained cancer-free without it.

New research published today in Nature Communications has found that analysing the body's own immune cells found near a tumour using new AI-based methods can provide additional insight to identify which patients can safely have chemotherapy withheld.

Currently, patients are assessed using a risk score, but the majority receive an intermediate result, meaning chemotherapy is often prescribed as a precautionary measure. To address this uncertainty, the researchers used samples from a randomised treatment trial comparing hormone-blocking therapy alone against hormone-blocking combined with chemotherapy in an Irish cohort of patients with intermediate risk scores.

Professor Darran O’Connor, research lead at RCSI School of Pharmacy and Biomolecular Sciences, explained the clinical importance of the findings: “For patients with an intermediate genomic risk, the decision around chemotherapy is often difficult and uncertainty frequently leads to treatment that may not have been necessary, impacting on quality of life.”

“Genomic testing has advanced our ability to tailor treatment for these patients, but AI-based analysis of the tumour microenvironment takes this further still. Crucially, because this approach works from tissue samples processed as standard, it has the potential to improve both the precision and the equity of treatment for most women with early-stage breast cancer, regardless of where they are treated."

The study found that a high density of cancer-targeting immune cells, called cytotoxic T-cells, in the tissue surrounding a tumour could more accurately define patient risk compared to current genomic profiling methods. Patients with high cytotoxic T-cell density were found to have poorer outcomes when treated with chemotherapy, suggesting its usefulness as a predictive marker for therapy effectiveness. RCSI and UCD have jointly filed a patent for the technology and is now seeking to commercialise the approach to support its translation into clinical practice.

Dr Zak Kinsella, first author on the study and Postdoctoral Researcher at RCSI said: "It's really encouraging to see how much additional prognostic information can be extracted from these samples using AI. The density of cytotoxic T-cells in the tumour microenvironment proved to be a remarkably strong predictor of treatment response, and that has real implications for how we approach chemotherapy decisions in this patient group."

Professor William Gallagher, Senior Author from the UCD School of Biomolecular and Biomedical Science and UCD Conway Institute, said: “Before this approach can be implemented in clinical practice, further validation in larger studies will be required. However, the findings give us a clearer picture of what drives recurrence risk of breast cancer in patients with intermediate genomic scores and brings us closer to the kind of personalised treatment decisions that could avoid unnecessary chemotherapy."

This research was carried out as a collaboration between RCSI, University College Dublin, Cancer Trials Ireland, Beaumont Hospital, St. Vincent’s University Hospital and Queen's University Belfast. The study was funded by Precision Oncology Ireland, a consortium that is part-funded by Research Ireland under their Strategic Partnership Programme. Further development of the technology is being funded by the ARC Hub for HealthTech, which is co-funded by the Government of Ireland and the European Union through the ERDF Northern & Western Regional Programme 2021-2027.

ENDS

For further information:

Laura Anderson, Senior Communications Officer, RCSI

087 199 0399/ lauraanderson@rcsi.ie

About RCSI University of Medicine and Health Sciences

RCSI University of Medicine and Health Sciences is ranked first in the world for its contribution to UN Sustainable Development Goal 3, Good Health and Well-being, in the Times Higher Education (THE) University Impact Rankings 2025.

Founded in 1784 as the Royal College of Surgeons in Ireland with responsibility for training surgeons in Ireland, today RCSI is an innovative, not-for-profit, international university exclusively focused on driving improvements in human health worldwide through education, research and engagement.

RCSI is among the top 300 universities worldwide in the World University Rankings (2026) and has been awarded Athena Swan Bronze accreditation for positive gender practice in higher education.

In 2026, RCSI opened Humanarium, a new public exhibition space and café at the heart of RCSI, exploring healthy futures for all, at 118 St Stephen’s Green in Dublin city centre. Through a rolling programme of free exhibitions and events exploring health sciences and medical research, it is a place for everyone to join conversations about how to live longer, healthier and happier lives.

Visit the RCSI Expert Directory to find the details of our experts across a range of healthcare issues and concerns. Recognising their responsibility to share their knowledge and discoveries to empower people with information that leads them to better health, these clinicians and researchers are willing to engage with the media in their area of expertise.

Journal

Nature Communications

DOI

10.1038/s41467-026-73432-2

Article Title

Spatial analyses implicate high stromal tumour-infiltrating CD8+ lymphocytes as a negative predictive marker for chemotherapy in estrogen receptor-positive breast cancer

Article Publication Date

23-Jun-2026

AI support tool improved clinician decisions in real-world primary care trial

AI-powered trial did not show statistically significant difference for patient outcomes but helped clinicians improve quality of notes and recommendations

University of Birmingham

A large real-world clinical trial has found that a generative AI-powered support tool used to support frontline clinicians was safe and improved the quality of clinical decision-making but did not significantly change short-term patient outcomes.

The study, published today in Nature Medicine is one of the first randomised controlled trials worldwide to test whether generative AI can improve patient-level outcomes, rather than just clinician performance or simulated cases.

The trial involved more than 9,600 patients attending 16 primary care clinics in Kenya, and was delivered by experts at the University of Birmingham supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre: Birmingham.

Clinicians were randomly assigned to use an electronic medical record system with or without an integrated AI consult tool that provided real-time diagnostic and treatment suggestions. The AI system, known as ‘AI Consult’, was a large language model–based clinical decision support tool embedded directly within the existing electronic medical record system.

During consultations, the tool worked in the background by:

Analysing information entered by the clinician into the medical record
Generating context‑specific diagnostic and treatment suggestions, aligned with Kenyan national clinical guidelines
Flagging potential concerns using a simple colour‑coded alert system (green, yellow or red)

Clinicians retained full autonomy; they were not required to follow the AI’s advice, and retained responsibility for all diagnosis, prescribing and referral decisions. The AI interface was not visible to patients, helping preserve normal patient–clinician interaction.

Senior author Professor Bilal Mateen, Honorary Professor of Machine Learning for Health at the University of Birmingham, and Chief AI Officer at PATH, said: “This is one of the first studies to rigorously ask the hardest question about AI in healthcare: whether it actually improves outcomes for patients.

“What we found is reassuring but also sobering. The technology appears safe and clearly improves aspects of clinical decision-making, but translating those gains into measurable patient benefit is much more challenging, particularly in everyday primary care.”

Serious outcomes such as hospitalisation or death are rare in primary care, meaning extremely large studies – potentially involving more than 100,000 patients – would be needed to detect modest effects.

Professor Alastair Denniston, co-author, Professor of Regulatory Science and Innovation at the University of Birmingham and lead for health data research at the NIHR Biomedical Research Centre: Birmingham, said: “A large part of primary care is to deal with common conditions, including those that are self-limiting, where many patients require low levels of healthcare intervention. In that context, even meaningful improvements in clinical reasoning may only result in small changes in patient outcomes that are very difficult to measure.

“What this study shows is that AI can be integrated safely into real clinical workflows, without undermining patient trust or clinician autonomy – which is a critical foundation for any future impact.”

Findings: safety, quality and costs

Researchers found no statistically significant difference in treatment failure within 14 days between patients seen with AI-supported care and those receiving standard care (2.2% vs 2.0%). The study found no evidence of harm, with similar rates of hospitalisation and death in both groups.

While the AI tool did not produce measurable improvements in short-term patient outcomes, it significantly improved the quality of clinical documentation and treatment planning, as assessed by an independent panel of experienced clinicians who were blinded to whether AI had been used.

Patient satisfaction was the same in both groups, suggesting that AI support did not alter patients’ experience of care.

The study also found that, although overall antibiotic prescribing rates were similar, antibiotic‑related costs were lower in the AI‑supported group, due to more cost-conscious prescribing choices.

Although the trial was conducted in Kenya, the researchers emphasise that the findings have global relevance, including for high-income health systems.

Professor Richard Riley, Professor of Biostatistics at the University of Birmingham and senior author, said: “Robust trials like this are so important to establish the real impact of using AI in practice. They help set realistic expectations of what AI can actually contribute within existing care pathways, and helps guide where future investment and research effort should be focused. Generalisability of our findings to higher-income settings, where baseline standards of care are already high, needs to be evaluated.”

The study was funded by the Gates Foundation, sponsored by PATH, and conducted with collaborators from the London School of Hygiene and Tropical Medicine and the KEMRI-Wellcome Trust Research Programme, Kenya.

Journal

Nature Medicine

DOI

10.1038/s41591-026-04503-6

Method of Research

Randomized controlled/clinical trial

Subject of Research

People

Article Title

Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster randomized trial

Article Publication Date

26-Jun-2026

AI-driven race strategy could give Formula One teams competitive advantage

King's College London

The AI model, developed by a research team led by a computer scientist at King’s College London, improved Lewis Hamilton’s finishing position for the Mercedes-AMG PETRONAS F1 Team in simulations of the 2023 Bahrain Grand Prix.

The scientists believe the AI could provide F1 strategists with additional information to inform their decision-making during races – where quick decision making based on data can improve performance on track.

Dr Antonio Rago, a Lecturer in Computer Science at King’s who began the research with a research team at Imperial College London before joining King’s, said: “We discovered that AI models were able to not only replicate both strategy and tactics from the real world, but also that they outperformed existing race strategy optimisation techniques in many cases.”

Race strategy is the art of making decisions that occur throughout the course of a race to gain an edge on rivals. This may include deciding when drivers should make pitstops and which tyres to use, in the hope of achieving the highest possible finishing positions.

Teams craft baseline strategies based on a variety of factors, such as their cars’ pace, the track surface, tyre degradation and weather. These strategies are then used in simulations to calculate the statistical likelihood of what is likely to happen in a race over thousands of runs.

However, because such simulations are so complex, they can take a long time to run. These simulations also fail to consider the complex interactions between teams’ strategies and tactics in the dynamic and unpredictable environment of the race, meaning they give limited accuracy.

Using a form of AI called reinforcement learning that uses trial and error to optimise the decisions it makes, the team trained the model using races across all tracks from recent seasons. These models were then supplemented by techniques from explainable AI, which provided the reasoning behind their decisions for F1 team strategists to review – encouraging them to have confidence in the models.

The models also exhibited “emergent tactics”, which involves the AI model learning to deploy real-world race tactics without being directly trained to do so. For example, the team saw convincing evidence of the models performing “undercutting”, where a car takes a pitstop earlier than the car ahead to take advantage of the pace offered by a newer set of tyres and overtake.

To test how well their newly developed methods worked, the research team took control of Lewis Hamilton’s Mercedes car in the 2023 Bahrain Grand Prix in a simulation and compared, over the course of thousands of simulations, their own model, named race strategy reinforcement learning (RSRL), with existing techniques which do not use AI.

RSRL had an average finishing position of P5.33, whilst the best techniques without AI achieved P5.63, a significant advantage given the fixed pace of the car (the teams’ cars had an expected finish of P5.50) and a large gain in a sport defined by tight margins.

It was also shown that training strategies can help fine tune the model to specific courses like Bahrain, while a more general approach could be applied to any circuit.

Dr Rago added: “Incremental gains are crucial in a sport considered the pinnacle of motorsport, and the trustworthy decision support provided by our AI models could free up race strategists to focus on other factors, maximising the teams’ chances of winning races. This is a very exciting time for the sport, and it may not be long before we see support from AI being crucial to winning championships.”

In the future, the research team hope to create and leverage AI models that explore how both drivers in an F1 team can cooperate on tactics, such as splitting strategies and attempting both an undercut and an overcut on the car ahead.

Dr Kedar Pandya, Executive Director of Engineering and Physical Sciences Research Council (EPSRC)’s Strategy Directorate, who supported the study, said: “This research highlights the growing potential of advanced AI to tackle complex, real-world decision-making challenges.

“By combining reinforcement learning, where systems learn through experience, with explainable AI that makes those decisions more transparent, Dr Rago and his colleagues have developed a powerful new approach to race strategy.

“Innovations like this, supported by EPSRC, are an important step towards wider adoption of AI in complex, multi-agent environments.”

Read the paper, published in the Special Issue on Discovery Science of Machine Learning Journal: https://doi.org/10.1007/s10994-026-07081-3

Elsevier’s ClinicalKey Student adds StudyFinder AI to quickly connect medical students to trusted, verifiable content for more efficient studying

Elsevier

London, June 25, 2026 – Elsevier has added StudyFinder AI to ClinicalKey Student to deliver trusted AI to medical education. 74%[1] of today’s medical students already use AI to support their studies, but many generic tools provide unverified answers lacking context, accuracy and transparency. Building on the launch of Osmosis AI earlier this year, the addition of StudyFinder AI allows students to use ClinicalKey Student to access trusted, evidence-based content quicker and more efficiently than ever before.

Addressing the growing demand for trusted AI in healthcare education, the enhanced ClinicalKey Student eliminates the risks often associated with generic AI alternatives. StudyFinder AI will initially be available in the UK, Australia and New Zealand, while Osmosis AI is currently available globally.

Brent Gordon, President of Elsevier Healthcare Education, said: “Most AI tools available to students today lack answers supported by trusted and verifiable sources. ClinicalKey Student is different − every StudyFinder AI response is drawn from content that medical schools recommend, including Elsevier’s evidence-based content, and is instantly verifiable. This is critical for students learning how to make clinical decisions.”

StudyFinder AI supports medical students in the following ways:

AI-driven search capabilities allow students to study more efficiently, quickly connecting them to trusted, curriculum-aligned resources
Embedded citations mean students can be confident they are using verified sources when studying
Allows students to ask questions and search topics, visualize with 2D and 3D models, and create visual aids for studying
Allows students to draw from a curated selection of evidence-based textbooks, over 6,000 multiple-choice questions and over 1,000 bite-sized videos

Nadya Affendy, a current UK medical student, said: “A key part of medicine is evidence-based learning which isn’t guaranteed by a lot of AI tools. That’s what stood out about StudyFinder; the AI bases itself off of trusted sources from Elsevier textbooks, as well as the NICE guidelines – the same kind of textbooks which are recommended by professors and lecturers throughout medical school.”

The announcement follows the launch of Osmosis AI in March this year, which provides the following capabilities:

Combines award-winning Osmosis videos, trusted Elsevier medical content (including the most authoritative textbooks) and responsible AI to provide timely support for students and educators
Connects answers directly to Osmosis videos students already rely on, and provides clear citations for verification
Prioritises high yield, clinically relevant explanations aligned with medical curricula and board exams

Elsevier Healthcare Education also plans to launch its AI-powered Assessment Creator this summer in select markets as part of a new enterprise solution for medical education, saving faculty valuable time when creating curriculum-aligned, evidence-based assessments.

Find out more about ClinicalKey Student here.

About Elsevier

Elsevier is a global leader in advanced information and decision support. For over a century, we have been helping advance science and healthcare to advance human progress. We support academic and corporate research communities, doctors, nurses, future healthcare professionals, and educators across 170 countries in their vital work. We help impact makers achieve better outcomes with research and clinical-grade solutions built on the world’s leading evidence-based scientific and medical content, precision AI, and expert human assessment. We champion inclusion and sustainability, working with the communities that we serve. The Elsevier Foundation supports research and health partnerships around the world.

Elsevier is part of RELX, a global provider of information-based analytics and decision tools for professional and business customers. For more information, visit www.elsevier.com and follow us on social media @elsevierconnect.

[1] Internal research study of 250 medical students (2025)

UW researchers created PaperTok, an AI system that helps users turn research papers into short, engaging videos

University of Washington

Recently, students in the University of Washington’s Prosocial Computing Group noticed a trend on social media: People were using generative artificial intelligence to make short science videos. The trouble was that these people weren’t scientists, which, given AI’s proclivity to be convincingly wrong, could accelerate the spread of misinformation. So the lab wondered how to enable scientists and other researchers to better adapt to platforms like TikTok.

“The alternative is that science is being talked about without scientists,” said co-lead author Meziah Ruby Cristobal, a UW doctoral student in human centered design and engineering.

Those discussions led the team to build PaperTok, an AI tool that helps users turn research papers into 45-second videos. A researcher uploads a paper to the tool, which uses Google Gemini to write a short script explaining the paper. The researcher can then iteratively edit the transcript and resulting video clip.

The team presented its research April 17 at the Association for Computing Machinery Conference on Human Factors in Computing Systems in Barcelona.

“For several reasons, most people don’t read research papers,” said senior author Gary Hsieh, a UW professor in human centered design and engineering. “I still have challenges reading papers in fields I'm not familiar with. So we wanted to find a way to quickly turn papers into a format that laypeople would want to engage with, and we wanted to study how they engaged with it.”

Currently, PaperTok is only accessible to users with a paid Google Gemini subscription. Those users can go to the PaperTok site and upload a research paper. The system then presents four options to use as a hook in the video. For instance, a PaperTok video on PaperTok itself begins, “Ever get overwhelmed reading a dense academic paper?”

“To start, we interviewed eight science communicators and content producers about how to make engaging, credible videos,” said co-lead author Donghoon Shin, a UW doctoral student in human centered design and engineering. “We found that hooks are integral to shortform videos. Because you're competing with other videos online, you have only a few seconds to grab someone’s attention.”

After picking a hook, PaperTok generates a script, which users can edit. In the storyboarding phase, the script is broken into scenes — much like a movie storyboard. Users can keep refining their scripts and matching video clips. When they’re happy with the result, they can add a byline, which appears at the end along with the paper’s authors.

The team asked 100 online participants and 18 academic participants to compare video from PaperTok with videos from two other PDF-to-video generators. They found PaperTok easy to use and its videos more engaging than those from the other systems. But some had concerns that it was “too AI-ish” — because of AI signs like nonsense text — to want to share publicly, because that may diminish their scholarship’s credibility.

The team plans to keep working on ways to customize the AI-generated video, such as allowing users to draw on specific parts of a scene so that elements change based on their intent.

“The main motivation behind PaperTok was, ‘How can we enable researchers to create engaging short-form videos?’” Cristobal said. “Because with generative AI tools, anyone can generate a video from a PDF in minutes, and that presents all sorts of problems — misinformation, AI slop. So we wanted to build a tool that keeps humans, ideally experts, involved. If anything, we hope that PaperTok highlights how important people are in science communication.”

Co-authors include Hyeonjeong Byeon, a UW doctoral student in human centered design and engineering; Tze-Yu Chen of Boson AI, who contributed to this research as a UW master’s student; Ruoxi Shang, a UW doctoral candidate in human centered design and engineering; Ruican Zhong, a UW doctoral student in human centered design and engineering; and Tony Zhou, a UW student in computer science. This research was supported by Microsoft AI and the New Future of Work Award, the Google PaliGemma Academic Program GCP Credit Award, and the National Science Foundation CISE Graduate Fellowships.

For more information, contact Hsieh at garyhs@uw.edu, Shin at dhoon@uw.edu and Cristobal at meziah@uw.edu.

DOI

10.1145/3772318.3790553

Article Title

PaperTok: Exploring the Use of Generative AI for Creating Short-form Videos for Research Communication

New white paper on closing the AI fluency gap to support workforce retention published by the University of Phoenix College of Doctoral Studies

New paper by Dr. Wayne L. McCoy examines how employers can turn AI skill development into a talent retention strategy

University of Phoenix

University of Phoenix College of Doctoral Studies has published a new white paper, “The Retention Mandate: Bridging the AI Fluency Gap to Secure the 2026 Workforce,” authored by Wayne L. McCoy, DM, MBA, and released through the Center for Educational and Instructional Technology Research (CEITR).

The paper examines a growing workplace challenge: employees are rapidly building artificial intelligence skills, while many organizations are still developing the policies, processes and career pathways needed to support AI-enabled work. Drawing on the 2026 Career Optimism Index® study and research on workplace psychology, technology readiness and organizational governance, McCoy argues that AI fluency is no longer only a productivity issue — it is a retention issue.

“Workers are not waiting for organizations to define the future of AI at work,” said McCoy. “Many are already learning, experimenting and building confidence with AI tools. The opportunity for employers is to create the structure around that energy with clear standards, practical training, manager support and career pathways that help employees see a future inside the organization.”

The white paper identifies what McCoy describes as an AI fluency gap: a disconnect between worker skill development and organizational readiness. It notes that employee-led AI learning can create mobility and confidence, but also uncertainty when job descriptions, policies, training systems and manager expectations do not keep pace.

What the white paper addresses

“The Retention Mandate” examines how organizations can better align people, processes, technology and data as AI becomes more embedded in the workplace. The paper highlights several factors shaping AI workforce retention:

Employee-led AI learning and “shadow learning”
AI’s impact on productivity, skills development and professional identity
Psychological safety and employee trust during AI adoption
Governance structures for responsible organizational AI use
Manager capability as a driver of employee confidence and retention

The paper proposes a four-step roadmap for employers seeking to strengthen AI readiness and retain AI-fluent talent:

Define AI career pathways and standards
Establish skills assessment systems
Expand training, tools and structured enablement
Build AI capability among managers

McCoy’s analysis positions AI adoption as a socio-technical transformation, not simply a technology rollout. The paper encourages organizations to pair AI implementation with clear governance, workforce development and leadership practices that support employee confidence, adaptability and long-term engagement.

About the author

Wayne L. McCoy, DM, MBA, serves as a dissertation chair and staff faculty member in University of Phoenix College of Doctoral Studies. He brings experience in business leadership, technology, entrepreneurship and higher education instruction. McCoy earned a Bachelor of Science in Information Technology, Master of Business Administration and Doctor of Management from University of Phoenix.

“The Retention Mandate: Bridging the AI Fluency Gap to Secure the 2026 Workforce” is available on the College of Doctoral Studies’ Research Hub.

About University of Phoenix

University of Phoenix is Built for Real Life. 50 Years Strong. The University innovates to help working adults enhance their careers and develop skills in a rapidly changing world through flexible online learning, relevant courses, academic AI pillars, and skills-mapped curriculum for associate, bachelor’s and master’s degree programs. Active students and alumni have access to Career Services for Life® resources including career guidance and tools. For more information, visit phoenix.edu.

About the College of Doctoral Studies

University of Phoenix’s College of Doctoral Studies focuses on today’s challenging business and organizational needs, from addressing critical social issues to developing solutions to accelerate community building and industry growth. The College’s research program is built around the Scholar, Practitioner, Leader Model which puts students in the center of the Doctoral Education Ecosystem® with experts, resources and tools to help prepare them to be a leader in their organization, industry and community. Through this program, students and researchers work with organizations to conduct research that can be applied in the workplace in real time.