Hi there,
The FDA has authorized over 1,350 AI medical devices. Every single one is a predictive, fixed-function tool — a model that takes inputs and returns a recommendation a clinician can review and override. ARPA-H is now selecting teams to build something fundamentally different: an autonomous agent that writes prescriptions, modifies care plans, and operates as a 24/7 cardiovascular care team member. The FDA has no framework for this. The governance problem is real, and it's not getting solved in 39 months without serious architectural investment.
🔥 Featured Post
FDA Has No Framework for Agentic Clinical AI. ARPA-H Is About to Create One.
- ARPA-H ADVOCATE is selecting teams in June 2026 to build the first FDA-authorized agentic clinical AI for cardiovascular care — connecting to EHRs, writing prescriptions, and acting autonomously around the clock
- FDA's January 2026 CDS guidance loosened oversight for recommendation AI — but only where clinicians can "independently review the basis for the recommendation." Autonomous prescription-writing agents cannot satisfy this condition
- FDA's SaMD lifecycle guidance was designed for adaptive, continuously-learning predictive algorithms — not agents that chain diagnosis, medication adjustment, scheduling, and patient communication into a single autonomous workflow
- ADVOCATE requires a supervisory agent to ensure the clinical AI's "consistent safety and effectiveness" — a meta-governance architecture that banking regulators just started wrestling with, and that FDA has never evaluated
- Healthcare AI architects deploying clinical decision support today need to understand that the governance gap ADVOCATE will expose is already present in the systems they're shipping
📚 In Case You Missed It
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Cursor Is Now SpaceX: Enterprise Agentic Coding's New Lock-In Risk — SpaceX's $60B Cursor acquisition ends model-neutral AI coding — enterprise teams built on Cursor's multi-model architecture now face a silent model substitution event in their agentic CI/CD pipelines.
OpenAI's Pre-Release Safety Trick: Make Models Think They're in Production — OpenAI replays 1.3M anonymized production conversations with candidate models before release — catching reward hacking that adversarial evals miss, with 1.5x median error in predicting undesired behavior rates.
More posts dropping every week. Stay sharp.
— Bhanu @ superml.dev
