1. FDA Opens the Door to a "Competency-Based" Review Standard for Generative AI Devices
REGULATION / UNITED STATES

What happened. On August 18, the FDA's Digital Health Center of Excellence released a discussion paper on regulating generative AI-enabled medical devices. It is a request for feedback, not draft guidance. Comments close October 19 under docket FDA-2026-N-7874.

Two ideas carry it. The first is a two-axis risk grid: how independently a tool acts, from information-only through recommendations to fully autonomous action, and how much harm follows from relying on a wrong answer. Evidence expectations rise with position on that grid, not with whether a language model is involved. The second is evaluation modeled on clinician credentialing — benchmarking, then clinical confirmation in progressively realistic settings, then ongoing assessment in use. A system with open-ended inputs cannot be tested on every scenario it will meet, any more than a clinician can. The 26 questions span postmarket monitoring, foundation models, and agentic systems.

Why it matters to the AIMed26 audience. This is the year's most consequential US regulatory signal, and it is worth reading precisely because it is an important development in AI in health regulation. Whoever amongst us comments before October 19 helps choose the words reviewers will later apply to everyone. Of note, this document is short on pediatric relevance. 

It is interesting that I had been suggesting to various organizations that we credential AI clinicians rather than AI models (similar to credentialing physicians who perform procedures rather than for each procedure). The credentialing metaphor brings the whole apparatus with it: benchmarks, defined scope of practice, periodic re-evaluation. Health systems already run that machinery for clinicians so should be able to execute this evaluation with enough expertise. 
The grid is usable tomorrow and requires every vendor to place its product on it in writing. Governance, not modality, now sets the burden of proof. Least discussed (but should be discussed proactively): whether accountability for foundation models sits with the developer or the integrator is unresolved, and it determines whether a health system building its own agents is a manufacturer or a customer as well. In my opinion, this evaluation as part of AI governance belongs to the board of directors and C-suite and not on the CIO or CMIO agendas. 

 

2. Epic's Agent Factory Makes AI Workflow Automation Default EHR Infrastructure
HEALTH SYSTEM DEPLOYMENT / AGENTIC AI

What happened. At Epic's Users' Group Meeting in Verona, Wisconsin (August 17–20), the company unveiled Agent Factory, a no-code platform with more than 120 prebuilt AI agents that organizations can deploy as shipped, tune locally, or extend with their own. General availability comes in 2027.

Judy Faulkner framed the wider strategy around Ergo — Epic's Latin "therefore" — an AI-native layer binding Art (clinician-facing for clinical workflow), Emmie (patient-facing for engagement), and Penny (administrative revenue cycle) into one interface that adapts to role and specialty. Early results are concrete. ECU Health reports roughly 20 hours a week saved by an agent summarizing transfer-center requests, plus an 86% increase in transfers landing at regional hospitals rather than the academic center.

Why it matters to the AIMed26 audience. Epic reaches more than 3,000 hospitals and roughly 325 million patients. When “agentic” AI (many AI agents are not “agentic” with autonomy) arrives as a configuration option inside the system of record rather than as a purchase, adoption stops being a build-versus-buy decision and becomes a governance decision. Big time. 
Ambient documentation was the entry point for provider-facing AI and has already commoditized. Agents are the next enclosure but this step requires much more AI governance and Epic infrastructure and database knowledge. This step also requires something I have recommended: please don’t use AI to automate a bad workflow or work process/design as AI will just make it faster but not necessarily better.

There is no published cost model for building inside Agent Factory, and therefore no calculable ROI for now. And no one answered the question that hung over the meeting: once anyone can spin up an agent without writing code, who keeps each one working? Maintenance of these agents to address drift is key. Lastly, just because there are multiple agents, it does not mean the system is “agentic” in behavior which implies some degree of autonomy. 

 
3. A Model Picks the Targets: First Phase 3 Win for an AI-Designed Therapy
AI-DESIGNED THERAPEUTICS / GLOBAL

What happened. On August 19, Merck and Moderna announced that intismeran autogene, given with pembrolizumab, met both endpoints in an interim Phase 3 analysis in adjuvant melanoma-less recurrence, less distant metastasis. It is the first randomized Phase 3 win for a neoantigen cancer vaccine, and the first for a therapy whose active ingredient is chosen by a machine learning model.

Every dose is computed before it is manufactured. Tumor DNA, tumor RNA, and normal DNA are sequenced and the patient's HLA type is called. A selection algorithm annotates the mutations, predicts which resulting peptides that particular immune system will actually present, ranks them for immunogenicity, and picks up to 34 from thousands of candidates. An automated step strings them into one mRNA construct. Moderna orchestrates the per-patient batch through a purpose-built digital system, Maestro; biopsy to injection runs roughly four to eight weeks.

Why it matters to the AIMed26 audience. This is truly amazing pioneering work on precision oncology enabled by AI; if this works, this AI-enable approach to cancer therapy will be truly disruptive. Nearly every prior claim for AI in drug development concerned speed and cost upstream: faster screening, cheaper candidates. Here the model's output is the therapy itself. This is the first randomized Phase 3 evidence that a prediction model can carry clinical benefit.
This precision approach also engenders several regulatory questions: What is approved when every dose differs and when there is a manufacturing process with a prediction model inside it. In addition, if the selection algorithm is retrained on new immunogenicity data, is it still the “same” product? This is precisely the problem the FDA raises about self-updating software.

The remaining bottleneck is computational logistics, not biology. Four to eight weeks from biopsy to dose is a scheduling problem, and health systems inherit it: sequencing, HLA typing, design turnaround, per-patient manufacture, and weeks or months of infusions. A supply chain management challenge for sure, but perhaps AI can offer a solution for the execution part as well.

 

4. Asia Builds the Yardstick: HIMSS Launches a Global AI Outcomes Framework in Singapore
MEASUREMENT / INTERNATIONAL — ASIA-PACIFIC

What happened. At HIMSS26 APAC in Singapore (August 23–25, co-hosted with SingHealth), HIMSS announced the AI Outcomes Framework, described as the first evidence-based, longitudinal standard for measuring the clinical, operational, and financial impact of healthcare AI.

The design is the news. Deployed AI is judged today on vendor-reported model metrics (AUC on a held-out set and accuracy against a retrospective cohort). The framework replaces that with pooled real-world data across health systems, tracking how models in production affect patient care, clinician cognitive load, and hospital throughput over time. The founding provider partners are entirely Asian: SingHealth and National University Hospital in Singapore, Asan Medical Center and Seoul National University Hospital in South Korea, and Taichung Veterans General Hospital in Taiwan. Worldwide rollout comes at HIMSS27, with peer benchmarks and clinical evidence published then.

Why it matters to the AIMed26 audience. I have long admired Singapore for its healthcare data handling and governance. Many governance committee members are stuck on the same question: how do we know this is working? Five Asian academic centers are supplying the founding evidence base, and the benchmarks they generate will become the peer comparisons your board eventually cites.

The United States leads on model development and deployment volume. Singapore, Korea, and Taiwan are leading on measurement- and measurement is what converts deployment into evidence, especially for outcomes. Some Asian health systems with unified national data layers can run longitudinal, multi-site outcome studies that fragmented American systems structurally cannot. Our advantage in scale is offset by our disadvantage in coherence. Clinician cognitive load appears as a first-class endpoint alongside throughput and cost, which is not what most US dashboards measure.
All of this points to a basic insight we often share and discuss: Good to great healthcare AI work relies on a strong foundation of healthcare data, especially longitudinal data. This is also why it is always good to have an international perspective on healthcare AI. 

5. Gates's Turbulence Memo: "The Greatest Equalizer, or the Worst Source of Injustice"
GENERAL AI — POLICY / SOCIETAL

What happened. On August 26, Bill Gates published a roughly 6,000-word essay, The turbulent AI era is here. The choices we make now are critical. Coming from technology's most durable optimist, the tone is the news: AI is advancing faster than governments and societies are preparing for, and even in the best case the transition will rank among the most turbulent periods in human history.

He names three risks. Labor displacement, which he distinguishes from earlier shifts because AI does not merely offload the task — it does the thinking, faster than institutions adapt. He expects law, medicine, software, and manufacturing to face serious disruption within a decade, with junior and mid-level roles most exposed. Malicious use, spanning cyber and biological risk. And developmental harm to children and to human relationships.
He proposes new national bodies and an international organization modeled on nuclear oversight and civil aviation; a protected class of "Human Reserved" occupations; and taxing AI and robotics to fund retraining.
Why it matters to the AIMed26 audience. 

A good but perhaps unrealistic idea is "Human Reserved," because it inverts the question our field has been answering by default. Governance committees ask where automation is safe. Gates asks which clinical work should remain human- not because a machine cannot do it, but because we decide it should not. Human performance becomes a value to preserve deliberately, the way one preserves a species on the verge of extinction. Some candidates are obvious. Comfort care at the end of life. Breaking bad news. The pediatric encounter where the parent needs a person more than an answer. Nobody has drawn that boundary; if the profession does not, procurement will.

His one healthcare example runs through demography rather than technology. Japan, with too few young people to care for the old, may welcome a caregiving robot a younger society would refuse. In addition, an extension of the developmental harm is the training of our junior colleagues. While AI promises to be a valuable partner for learning, loss of critical thinking and clinical judgment can occur unless we plan to preserve those dimensions.