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
image:
Dr. Nelly Elsayed, associate professor in the University of Cincinnati School of Information Technology.
view moreCredit: Photo/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
Parallel AI slashes energy costs and carbon emissions in wind-solar-hydrogen power systems
image:
Schematic diagram of the distributed reinforcement learning dispatch framework for wind-solar-hydrogen systems
view moreCredit: 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).
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
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
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
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
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
UW researchers created PaperTok, an AI system that helps users turn research papers into short, engaging videos
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.
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 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.
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.
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