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When X-Rays Talk Back: Grounding AI Diagnosis in Evidence, Not Eloquence

Chest X-rays are not mysterious objects. They are images that radiologists interrogate through a disciplined sequence: find the anatomy, measure what matters, compare against criteria, and then make a diagnostic judgment. The modern vision-language model often skips the middle of that sequence. It looks at the image, produces a polished explanation, and hopes the reader will not ask too aggressively where the evidence came from. This is how medical AI becomes impressive in a demo and uncomfortable in a clinic. Fluency is cheap. Verifiability is expensive. ...

February 27, 2026 · 14 min · Zelina
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Charting a Better Bedside: When Agentic RL Teaches RAG to Diagnose

TL;DR for operators Diagnosis is not a search-box problem. A clinician does not simply type a symptom list, read a guideline, and pick a disease like ordering takeaway. The useful work is iterative: form a hypothesis, compare against similar cases, notice what does not fit, retrieve again, ignore plausible-looking rubbish, and only then commit. ...

August 24, 2025 · 18 min · Zelina