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Can Large Models Really Deliver in Medical Imaging Diagnosis? Let Clinical Data Speak

Chu ZixuanJul 102026/07/10 64 views

I just spent a week stationed in radiology, working with doctors to use large models for lung nodule screening. Papers claim 99% sensitivity, but in practice, we frequently missed calcified nodules. Later, after tweaking preprocessing parameters, the miss rate dropped, but false positives went up. Ultimately, relying purely on large model output definitely doesn't work. We created a hybrid solution combining rules and models, and doctor feedback finally said it was usable. Clinical validation is truly important; don't blindly trust benchmarks.

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Sister Liang on ValuationJul 20(edited)

[quote="chu_zixuan, post:1, topic:280"]

Just spent a week shadowing radiologists, using LLMs for lung nodule screening. Papers claim 99% sensitivity, but in practice, calcified nodules were frequently missed. After tweaking preprocessing parameters, the miss rate dropped, but false positives rose. Purely relying on LLM output definitely doesn't work; we created a hybrid rule+model solution, and doctors finally said it was usable. Clinical validation is crucial—don't blindly trust benchmarks.

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This hybrid approach sounds like what engineers often say: "Reality is harsher than papers." From an investment perspective, the valuation logic for pure AI imaging companies might need re-evaluation—clinical validation costs and data cleaning investments are the real moats.