Generative AI is now deeply embedded in healthcare (whether you see it or not). It’s increasingly being used to draft clinical notes, summarize radiology reports, and fill electronic health records.

These tools save time, so no wonder they’re being rapidly adopted. But a new multi-institutional preprint from researchers at Harvard University, Stanford University, Mayo Clinic, and the National University of Singapore suggests the bigger risk lies ahead.

Medical AI may slowly degrade when it begins learning from its own outputs.

What the study tested: The researchers simulated repeated training cycles where medical AI systems learned from AI-generated data instead of verified human records.

They tested this across clinical notes, chest X-ray reports, and synthetic medical images used for training diagnostic models.

What they learned was that the language became generic. Rare but clinically important findings faded. Outputs converged toward safe-sounding defaults.

In radiology, serious conditions such as pneumothorax and lung masses were increasingly omitted from reports, even when visible on scans.

Clinical notes lost medication instructions, follow-up guidance, and specificity after only a few generations.

Why this is hard to catch: Standard evaluation metrics did not flag the problem. On synthetic data, the models appeared to improve. Confidence increased. Performance collapsed only when tested against real patient data.

The authors describe a growing gap between confidence and correctness. In medicine, that gap is dangerous because errors of omission are easy to miss.

Why you should care: AI-generated medical records already feed real-world evidence studies, safety monitoring, patient stratification, and trial planning.

If those records become increasingly flattened over time, rare adverse events and subgroup signals risk disappearing from downstream analysis.

If medical AI keeps learning from itself, we will need new ways to protect clinical nuance!

Source: [Research Article]

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This week, Goodfire raised $150 million in a Series B funding at a $1.25 billion valuation, betting that the next leap in AI will come from understanding models, not just scaling them.

Most AI systems today operate as black boxes. Goodfire focuses on interpretability, the ability to inspect how neural networks represent concepts internally and to modify those mechanisms directly.

That approach has already produced tangible results. By reverse-engineering a scientific foundation model, Goodfire helped identify a new class of Alzheimer’s biomarkers, a rare case of AI yielding novel biological insight rather than just predictions.

The company is now applying the same techniques to model design, showing it can reduce hallucinations by directly retraining internal model components.

With new funding, Goodfire plans to build a model design environment aimed at making AI systems more reliable, steerable, and scientifically useful.

As AI spreads into medicine, science, and policy, interpretability is emerging as a control layer that the industry can no longer ignore.

Sources: [Article]

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