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Self-supervised learning outperforms clinical supervision in aligning medical image representations, challenging assumptions about model interchangeability.
Text-only models can rival multimodal counterparts in chest radiography accuracy, questioning the necessity of image input for clinical AI applications.
LLM agents can leap from 40% to 88% accuracy in complex clinical tasks simply by validating new skills against a regression budget, proving that *how* you learn matters more than *what* you learn.
Clinicians trust AI recommendations nearly 3x more when those recommendations are broken down into verifiable facts linked to source guidelines, blowing traditional explainability out of the water.
