Fully autonomous AI in medicine demands rigorous safety frameworks, not just performance benchmarks

Autonomous medical AI systems are being deployed but may lack adequate real-world safety monitoring, raising concerns that current regulatory pathways and anomaly detection methods are insufficient for independently operating systems.

• Commentary piece argues that autonomous AI introduces distinct safety risks beyond standard diagnostic accuracy failures, such as silent failure modes without human oversight.
• Suggests that post-market surveillance and 'magic pudding' assumptions (i.e., that more data always fixes problems) are inadequate for high-stakes, fully autonomous systems.
• Limitation: Opinion/essay format without new empirical data.

Automated summary

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