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arXiv AI By Silas Ruhrberg Est\'evez, Nicol\'as Astorga, Mihaela van der Schaar

Timely Clinical Diagnosis through Active Test Selection

Read the original on arXiv AI →

The paper introduces ACTMED, a diagnostic framework that combines Bayesian Experimental Design with large language models to emulate real‑world clinical reasoning. ACTMED actively selects the most informative test at each step, using LLMs to simulate patient states and update beliefs without needing task‑specific training data. The authors evaluate the system on real datasets, demonstrating improvements in diagnostic accuracy, interpretability, and efficient resource use while keeping clinicians involved in the decision loop.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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