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September 26, 2026

Takeaways from the most recent news in the technology and policies shaping healthcare.

Health IT

Who Is Liable When Clinical AI Gets It Wrong?

A batch of letters published by STAT News crystallizes a question the healthcare industry has yet to answer: when an AI tool influences a medical decision and something goes wrong, who is responsible?

One reader put it bluntly, telling STAT that "a click, signature, or brief review should not magically transfer responsibility from an AI developer or platform to a physician." The concern is that vendors are building products designed to keep a human in the loop, then using that human's rubber-stamp approval as a legal shield. In practice, physicians are being asked to vouch for recommendations they cannot fully inspect or audit, while developers retain control over the underlying models.

The letters, which also touched on M.D. versus D.O. training, surrogacy, and other topics, reflect a broader unease as AI moves deeper into diagnosis and documentation. Until liability frameworks catch up, the risk falls unevenly on clinicians. Readers want accountability tied to whoever actually controls the system, not just the last person to click approve.

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Health IT

Pointcore Launches Perception to Turn Hospital Data Into Action

Pointcore's Perception platform aggregates hospital operational data and uses AI modeling to help leaders make faster, better-informed operational decisions.

Why it matters: Hospitals sit on vast operational data but rarely turn it into action, and tools that close that gap could reshape how systems manage cost and capacity.

Health IT

Tennr Hires CRO John Capaldi to Scale Sales Push

Patient orchestration platform Tennr hired John Capaldi as CRO and two enterprise sales executives to scale its go-to-market organization.

Why it matters: The hires show a well-funded health-tech startup pivoting to aggressive enterprise sales, a sign of maturing competition in referral and intake automation.

Health IT

AI Turns Messy Clinical Data Into Research-Ready Variables

AI and natural language processing are being deployed to convert fragmented, unstructured clinical data into structured, research-ready variables, easing a core bottleneck in clinical research.

Why it matters: Faster, cleaner data extraction can shorten trial timelines and unlock real-world evidence from records healthcare organizations already hold.