Saturday, October 17, 2026

JMIR News: Informed consent’s redesign for AI-related trials, an autonomous oxygenation device, and the physician-led campaign for Canada’s Safe Social Media Act

JMIR Publications

Toronto, October 16, 2026 — JMIR Publications this week released three News and Perspectives features on the latest digital health news: stories on how informed consent must be rethought for clinical trials involving AI; Canada’s proposed Safe Social Media Act and the Canadian Medical Association’s support; and a promising new autonomous oxygen titration device for health care.

Designing Informed Consent for AI Trials

In "Informed Consent in the AI Age: Why and How Documentation Must Change", JMIR Correspondent and researcher Hankun Su argues that traditional informed consent frameworks, which tend to assume that risks to patient safety are static and that clinicians will be able to fill in any gaps in understanding, are inadequate for clinical trials that use AI. “Documents must anticipate questions clinicians cannot answer and provide accessible responses within the text itself,” he writes. Su and his colleagues developed an informed consent framework for AI-related clinical trials that mandates transparency and disclosure, uses tiered standards based on risk, and employs dynamic consent mechanisms—one of many possible frameworks. “The deeper imperative,” writes Su, is recognizing that participant autonomy in AI trials depends on information ecosystems designed for the world as it actually functions, not the world as we wish it were.”

The Canadian Medical Association’s Campaign for Social Media Safeguards

JMIR Correspondent Wendy Glauser reports on a physician-led campaign in favour of Canada’s Bill C-34 in "Canadian Medical Association Calls for Social Media Policies to “Heal Childhood””. Bill C-34, if passed, would “ban children under 16 from creating social media accounts, unless the platform can show that it has adequate safeguards in place to protect children’s mental health…[and] require AI chatbots and social media platforms to better prevent cyberbullying and content that encourages self-harm or sexually victimizes children,” writes Glauser. She speaks with physicians on the upticks in mental health issues they’ve observed in their offices, as well as with psychology researcher Amori Mikami, PhD on the complexities of social media’s effects—good and bad—on mental health.

Offloading Oxygenation

Hypoxemic patients need their supplemental oxygen flow to be adjusted repeatedly to maintain a healthy blood oxygen level, creating a critical but time-consuming task for health care workers. In "Could Autonomous Oxygen Titration Help Patients—And Busy Care Teams—Breathe Easier?", JMIR Correspondent and ICU nurse Jenna Congdon covers a promising closed-loop device that uses feedback from a pulse oximeter to titrate patients’ blood oxygen levels to a set target. A recent clinical trial evaluating this device found that the automated oxygenation control “significantly reduced time spent in both hypoxemia and hyperoxemia” (low and high blood oxygen, respectively). Still, writes Congdon, “autonomous titration still operates within clinician-prescribed parameters…it raises a question about who holds ultimate responsibility when things go wrong.”

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.

Media Contact:

Dennis O’Brien, Vice President, Communications & Partnerships

JMIR Publications

communications@jmir.org

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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

Subject of Research

People

The crucial responsibilities doctors must never offshore to AI

University of Virginia Health System

With artificial intelligence making deep inroads into healthcare, two top doctors are urging physicians to embrace the technology but to remember that only humans can serve the best interest of individual patients.

The University of Virginia School of Medicine’s Andrew S. Parsons, MD, MPH, and Harvard Medical School’s Adam Rodman, MD, MPH, note in a new commentary that AI is growing increasingly sophisticated at an astonishing pace. The technology can already match or exceed human doctors at certain tasks, such as making diagnoses from written cases and electronic health records. That has prompted debate about whether AI will soon replace doctors for many applications.

Parsons and Rodman, however, note that deciding how to manage a patient's care -- choosing treatments, tests and follow-up that fit the patient's goals and circumstances - cannot be reduced to a “correct” answer. AI may be able to identify a patient’s medical condition, but deciding what to do next means choosing which risks a particular patient should accept, and a recommendation that works in theory can fail in practice.

“Guidelines and trials skew toward the populations studied, and real patients frequently fall outside those norms because of complex comorbid conditions and social circumstance or because their goals differ from a trial’s end points,” Parsons and Rodman write. “Deviating from the technically correct answer is often good medicine, and someone must answer for that deviation.”

And there lies the real role for human doctors in the medicine of tomorrow, the authors argue. Even as AI takes on more of diagnosis, doctors must consider each individual’s circumstance, work with patients to make hard choices, then shoulder the responsibility for the outcomes.

“We contend that the clinician’s enduring role is accountability: owning the risk of acting under uncertainty and answering for the decision that follows,” Parsons and Rodman note. “That ownership is real only when the clinician still reasons about the decision and holds a stake in its outcome.”

This work, they say, cannot be delegated to AI, however accurate its diagnoses become.

“A clinician who knows they will answer for the result weighs options differently than a system with nothing to lose,” Parsons and Rodman write. “A clinician who expects to see the patient again also considers which choices will preserve the relationship if things go badly. Artificial intelligence will keep improving the information that feeds these decisions, but it cannot take ownership of what happens next.”

Human doctors, they conclude, should welcome AI's growing accuracy while continuing to own the risk of acting for each patient. It is, after all, the patient and not the computer who will have to live with the consequences.

About the AI Commentary

Parsons and Rodman have published their commentary in the Annals of Internal Medicine. Parsons has no financial interest in the work; Rodman has done unrelated work for Google.

To keep up with the latest medical research news from UVA and UVA’s new Paul and Diane Manning Institute of Biotechnology, bookmark the Making of Medicine blog at https://www.uvahealth.com/making-of-medicine.

DOI

10.7326/ANNALS-26-02478

Pharma.AI fall 2026 release recap: transforming drug discovery and development through specialized models and AI agents

InSilico Medicine

Insilico has a connected set of capabilities for adapting AI to scientific work. MMAI Gym develops specialized models; benchmarking helps researchers assess their performance; Generative Biologics, Chemistry42, and PandaOmics provide scientific tools; and MCP connects those tools to AI assistants. The practical value is a path from scientific specialization to research workflows that teams can inspect and direct.

MMAI Gym turns foundation models into scientific specialists

MMAI Gym for Science is Insilico’s scientific post-training and evaluation framework. Post-training means taking a model that has already learned general capabilities and further adapting it to defined scientific tasks. The Gym uses scientific data, reasoning examples, and domain-focused training to develop models that can work on problems in drug discovery and development.
This gives the company both a framework for improving models and a family of specialists built for particular scientific jobs. The five specialists shown in the webinar serve different decisions in a discovery program:

Kinase activity prediction estimates compound activity against kinases, helping researchers compare compounds within this important family of biological targets.
GPCR activity prediction assesses compound activity across G protein-coupled receptors, supporting lead prioritization and receptor selectivity assessment.
ADMET prediction estimates properties relating to absorption, distribution, metabolism, excretion, and toxicity, helping teams assess compound behavior before further testing.
Single-step retrosynthesis proposes possible chemical precursors for a synthesis step, supporting chemists as they consider how a molecule could be made.
Therapeutic target discovery helps prioritize biological targets for investigation in a disease, supporting the decision about where a discovery program should focus.

The reported results show why specialization matters. Insilico described performance matching or exceeding the state of the art across more than 70 chemistry and biology benchmark tasks. In the ADMET demonstration, InsilicoMMAI-4B-Chem-ADMET competed with established specialist methods and showed lower prediction error on several displayed endpoints. These are examples of task-specific scientific performance.

Evaluation makes model performance useful to researchers

DDD Benchmark provides a common setting for comparing general-purpose models and scientific specialists using consistent datasets, splits, and metrics. Researchers can examine results by scientific domain and individual task, so a model’s strengths can be considered in relation to the work they need it to do. The company reported 302 benchmarks across six R&D domains.

The O3DC complements this with an open index of benchmarks, their scope, and their known caveats. It helps the community assess whether a benchmark is suitable for a scientific question. The benchmarking service described in the webinar also lets teams assess their own models through a protected endpoint, receive private reports, and retain the model in their own environment.

The compact Liquid model

LFM2-2.6B-MMAI, developed with Liquid AI, is a compact chemistry model built through the Gym. The company presented it as available for direct testing, with efficient on-premise deployment and state-of-the-art performance on selected tasks. Its 2.6-billion-parameter size matters because teams can consider a smaller scientific model for their own infrastructure, where deployment requirements and data control are important. It is a separate model from the 4B ADMET specialist shown in the comparisons.

The Virtual Aging Cell

The Virtual Aging Cell is an age-aware, multi-agent simulation platform for exploring how an intervention may affect human biological systems. The company described a system, orchestrating of specialist agents working within a shared causal graph across molecular, cellular, tissue, systemic, and phenotype levels. Specialized agents consult scientific literature and clinical databases, then connect proposed effects across these levels while retaining the evidence and reasoning behind each connection.

Generative Biologics: Epitope reasoning and MCP connectors

Generative Biologics provides tools for designing, predicting properties of, and refining antibodies, nanobodies, and peptides. The new Epitope Prediction workflow addresses a practical question after a binder has been identified: where is it likely to interact with its target? It returns ranked predicted binding regions and inspectable structural models, giving researchers hypotheses they can compare and investigate.

Its MCP demonstration showed an agent evaluating existing antibody variants and preparing a shortlist for experimental validation based on the developability properties. The value is a clearer basis for choosing what to test next, with predictions, visualizations, and downloadable results available for review. The scientific output remains a computational recommendation until tested experimentally.

Chemistry42 connects molecular design with synthetic route planning

Chemistry42 combines generative AI and physics-based methods for small-molecule research. It brings molecular generation, property assessment, synthesis planning, and further calculations into one environment. Typed Excluded Volumes gives researchers spatial controls to guide molecular design according to their scientific hypothesis.

ChemCensor adds chemical-plausibility assessment to Retrosynthesis, the process of working backward from a desired molecule to possible ways to make it. It evaluates support from documented synthetic precedents, helping chemists scrutinize proposed routes. On the 100-target URSA-expert-2026 benchmark, the company reported a 55% Solv-2 score for Chemistry42 Retrosynthesis plus ChemCensor, compared with 32% for RetroChimera MCTS and 29% for AiZynthFinder MCTS. This is a result for a defined computational route-planning benchmark.

PandaOmics turns biological data into structured analysis

PandaOmics connects omics and biomedical data with target discovery and drug program evaluation. Its Agent is now the default interface described in the webinar: researchers can state a question in plain language and receive an analysis with supporting evidence. The new skills address three related research needs:

Single-Cell Signature compares gene expression across cell types and disease states, making biological differences easier to examine.
Indication Prioritization brings together disease rankings, biological rationale, genetic evidence, and the drug and clinical-trial landscape in a structured report.
CADD Structural Review assesses a target’s structural druggability and tractability, helping researchers consider its potential for structure-based discovery.

Additionally, PandaOmics Classic introduces Score Weights in TargetID, enabling researchers to adjust how evidence contributes to target rankings. Saving these preferences and including them in CSV exports makes the basis for a ranking easier to retain and explain.

MCP connects the scientific engines to AI assistants

Model Context Protocol, or MCP, provides a connection through which an AI assistant can call software tools. In Pharma.AI, it lets compatible clients such as Codex, Claude Code, and Cursor access the scientific platforms. Researchers describe a task; the agent coordinates tool calls and returns the results into the conversation for review and follow-up work.

The Chemistry42 demonstration linked molecule generation, profiling, candidate selection, and further calculations while retaining project context and allowing configuration review. This illustrates how scientific tasks can follow one another without manually rebuilding the workflow at each step. The platform tools perform the specialist work; the assistant coordinates it.

AI adoption and evaluation toolkit created to help law firms improve their use of artificial intelligence

Aston University

Aston University researchers contribute to AI adoption and evaluation guidance
Will help legal organisations use artificial intelligence with more and clarity
Toolkit is grounded in hands-on testing carried out by legal professionals using real-world legal scenarios.

Aston University researchers have contributed to an AI adoption and evaluation guidance to help legal organisations assess, implement and increase the use of artificial intelligence with more confidence and clarity.

It has been developed by SuperTech, a cluster organisation driving technology and innovation-led growth for financial and professional services across the West Midlands. They have worked alongside law firms Fieldfisher, HCR Law, and Mills & Reeve, and refined with academic input from Aston and Oxford Brookes universities.

The toolkit provides legal professionals with a structured framework for evaluating AI tools, identifying suitable uses and developing a realistic adoption strategy. The toolkit is grounded in hands-on testing carried out by legal professionals using real-world legal scenarios.

The resource has been developed to support a broad range of organisations across the legal sector, with a particular focus on smaller law firms, legal clinics, pro bono services and other organisations that may not have access to dedicated technology or AI specialists. It also provides valuable guidance for lawtech vendors seeking to better understand the needs and expectations of legal professionals.

It includes a comprehensive evaluation ‘scorecard’ that enables organisations to assess legal artificial intelligence solutions across eight key dimensions, including areas such as core legal quality, drafting and output quality, workflow and supervision fit, efficiency and value. Designed to be practical, repeatable and technology-neutral, the framework helps organisations compare products consistently, identify where AI can deliver genuine value and make more informed investment decisions. Whether firms are taking their first steps with AI or looking to scale existing adoption, the toolkit has been designed to be used across different teams, seniority levels and stages of AI maturity.

In addition to the evaluation framework, the toolkit includes practical legal AI use cases, example prompts and testing methodologies and guidance on interpreting evaluation scores. It also incorporates adoption and implementation advice best-practice approaches for engaging technology companies and recommendations for developing organisational AI readiness.

Alexeis Garcia Perez, professor of digital business and society at Aston University said: “Drawing on Aston University’s expertise in digital transformation and responsible innovation, we have helped develop an evidence-based framework that enables legal organisations to evaluate AI rigorously and identify where it can deliver sustainable value.”

David Hall, Partner, Technology and Innovation at HCR Law said: “It’s vital for legal services providers who want to use AI to equip themselves with knowledge and tools for evaluating AI products, and be able to engage with a range of staff and stakeholders, not just those who are comfortable with AI or want to use it. SuperTech’s toolkit supports this effort, gets the key factors on the table, and can be adapted to address more. It also embeds a key discipline: evaluation should be measurable, and already focused on finding and evidencing ROI. We used the toolkit to evaluate almost 10 legal AI products. It gave us confidence in our evaluation, and helped us refine what we want from AI products. We are looking at further developing and using the toolkit in our business going forwards."

Hilary Smyth-Allen SuperTech CEO added: "This toolkit reflects SuperTech's approach to innovation: bringing the legal sector together to test ideas, share learning and build confidence through real-world collaboration. Developed through our Innovation Lab, it combines insights from law firms, legal technology innovators, academics and sector partners to provide practical guidance tailored to the needs of legal teams looking to adopt AI responsibly and effectively."

Electrical engineering research unlocks DNA mystery

Texas A&M University engineers make groundbreaking discovery about the origins of life and establish a framework that improves research outcomes.

Texas A&M University

Scientists have long sought to understand the complex origins of intelligent life. Emerging during the hostile climate of primordial Earth, it remains unclear how biological systems not only formed but survived and persisted under extreme conditions.

Now, researchers at Texas A&M University are using light-based technologies to uncover new clues to help solve this evolutionary puzzle.

Dr. Peter Rentzepis, a professor in the Department of Electrical and Computer Engineering, and his research assistants Keyvan Khosh Abady and Negar Karpourazar, have achieved a significant breakthrough on the origins of molecular evolution. Their findings, published in Proceedings of the National Academy of Sciences (PNAS), offer new insights into the beginning of life on Earth.

Unraveling a decades-long puzzle

When intelligent life formed, DNA became the primary biological code, diverging from single-stranded RNA. When this occurred, a chemical called thymine replaced uracil as one of the molecule’s main building blocks.

However, this change has long perplexed scientists, as thymine is more prone to ultraviolet (UV) damage. This would seemingly make it a poor replacement on early Earth, where the lack of a protective ozone layer exposed lifeforms to extreme UV radiation from the sun. The apparent paradox prompted Rentzepis’ team to investigate why Mother Nature made such an unconventional choice.

The team launched a study comparing thymine and uracil under controlled UV conditions designed to mimic the intense radiation environment of primordial Earth. Using biophotonics — an interdisciplinary field that uses light-based spectroscopic methods, including absorption, fluorescence and Raman spectroscopy — to analyze the molecules, they made a significant discovery.

“We found that thymine tends to direct UV damage into a more reversible form, suggesting that early genetic molecules may have survived intense sunlight not by avoiding damage, but by controlling it and making it easier to self-repair,” said Khosh Abady. “In that sense, thymine may have acted as a molecular sunscreen, absorbing harmful UV energy and keeping the damage confined to the genetic bases rather than allowing it to harm more vulnerable parts of the molecule.”

Microscopic discovery, big implications

The ongoing scientific implications of this discovery are vast, affecting a variety of areas of study as researchers continue to learn more about who we are and where we came from.

Measurements developed by the team also establish a new reference framework that could eventually support important research like detecting pathogens and checking whether UV disinfection systems are working properly.

“The distinct spectral signatures we measured could support applications in faster, more sensitive pathogen detection and in monitoring whether UV disinfection has effectively inactivated microorganisms, particularly in water-treatment systems,” said Khosh Abady.