LLMs fail at information-seeking in clinical reasoning, study finds

Jul 14, 2026

A new study on arXiv finds that large language models (LLMs) struggle with proactive information-seeking in clinical reasoning, achieving only 68% accuracy on an agentic oncology task. The main limitation identified was not medical knowledge, but systematic failures such as anchoring and premature closure, which mirror cognitive biases seen in novice clinicians.

Why it matters: This highlights that LLMs' primary weakness in clinical settings is their inability to seek and utilize critical information under uncertainty, a key component for accurate diagnosis and treatment.

Full story at: arXiv AI/ML ↗