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Self-supervised learning outperforms clinical supervision in aligning medical image representations, challenging assumptions about model interchangeability.
CANDOR reveals that even top-performing encoders misclassify nearly 18.4% of positive cases, challenging the notion of their reliability in clinical settings.
Specialization in vision-language models dramatically increases the risk of re-linking de-identified medical images to their original reports, revealing critical privacy vulnerabilities in clinical AI applications.
Scaling clinical LLMs doesn't guarantee safety: high-risk errors persist even with advanced RAG and max-context prompting, highlighting the critical role of evidence quality and deployment strategy.
Even with multi-agent adjudication, MLLMs still struggle to reliably differentiate visually confounded diseases in a zero-shot setting, highlighting limitations in current agent-based diagnostic approaches.
