AI in Healthcare
The latest on artificial intelligence transforming medicine
News stories discovered and organized by an automated pipeline. Covering clinical deployments, research breakthroughs, regulation, and industry developments.
The hidden FDA problem with AI medical devices is not approval — it’s what happens after
STAT reports that AI medical devices have a ‘dirty FDA secret,’ pointing to the gap between clearance and real-world performance. The story suggests that regulation may be strongest at the moment of approval and weakest once systems are deployed, updated, or used in new settings. That gap is where many of the most important safety questions now live.
Post-Launch Monitoring Is Becoming a Core Test of Medical Device Credibility
A new discussion of post-launch monitoring argues that success in medical devices no longer ends at clearance or launch. Companies now need stronger surveillance, feedback loops, and lifecycle management to prove their products remain safe and effective in the real world.
ACR and SIIM Pair AI Practice Guidance With a Registry Designed for Real-World Monitoring
The ACR and SIIM have approved an AI practice parameter and introduced the Assess-AI registry to track real-world use of imaging algorithms. The move underscores how rapidly radiology is building infrastructure for oversight, not just adoption.
FDA's push for AI safety monitoring could reshape how medical devices stay on the market
The FDA is asking industry how to monitor AI medical devices after approval, signaling that premarket clearance is no longer the end of the regulatory story. The move reflects a broader shift toward continuous oversight as algorithms update, drift, and encounter new real-world conditions.
FDA Seeks Industry Input on How to Monitor AI Medical Devices After Clearance
The FDA is asking industry how best to monitor AI-enabled medical devices after they are cleared, signaling that post-market surveillance is becoming central to oversight. The move reflects a broader recognition that AI performance can drift over time as data, users, and workflows change.
FDA Warning on a Vascular Device After Three Deaths Highlights the Limits of Device Oversight
The FDA’s warning about a vascular device after three deaths is a stark reminder that device safety problems still emerge after products reach the market. In the AI era, it also underscores why post-market surveillance is becoming a central concern for regulators and providers alike.
Ethical AI in Radiology Is Becoming a Post-Market Responsibility
A radiology ethics discussion is shifting the focus from algorithm performance to the full lifecycle of responsibility: people, deployment, and post-market monitoring. That reflects a broader reality for healthcare AI, where safety is increasingly defined by what happens after launch.
FDA Guidance Changes Put Medtech AI Teams on Notice
A new industry analysis says FDA’s AI device guidance is evolving in ways that will force medtech companies to tighten documentation, monitoring, and change management. The underlying message is clear: AI products will be judged less like static tools and more like living systems.
FDA Tells Industry to Stop Treating AI Like Static Software
A Cato Institute analysis argues that the FDA’s current software framework is poorly suited to AI systems that evolve, retrain, and behave differently across settings. The piece adds to a growing policy debate over whether regulators need a more adaptive model for software-as-medical-device oversight.
Real-World Evidence and Change Control Plans Are Emerging as the Missing Infrastructure for Adaptive Digital Health
A new analysis argues that real-world evidence and predetermined change control plans could accelerate adoption of digital health technologies, especially those that evolve after launch. The idea is increasingly central to AI regulation: if software can change, the oversight model has to account for controlled change rather than freeze products in time.
FDA’s lighter-touch digital health stance may speed innovation—but shift pressure to evidence and governance
A Healio Q&A suggests the FDA is loosening aspects of oversight for digital health innovation, reflecting a more adaptive posture toward software-driven care tools. That could accelerate product iteration, but it also increases the burden on developers and providers to prove safety, monitor performance, and govern real-world use.
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An automated pipeline searches the web for significant AI healthcare news across clinical, research, regulatory, and industry domains.
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