arXiv:2604.14892v3 Announce Type: replace-cross Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators. Here, we evaluate an LLM Jury, composed of three frontier AI models, for scoring 3334 diagnoses on 300 real-world low- and middle-income country (LMIC) hospital cases. Both LLM- and clinician-generated diagnoses are scored against expert panel diagnoses across four dimensions: diagnosis, differential diagnosis, clinical reasoning, and negative treatment risk. The LLM Jury scores
The increasing sophistication of LLMs and the high cost of human expert evaluation in medical AI are driving the immediate need to explore alternative assessment methods.
This development suggests LLMs could significantly accelerate the development and deployment of medical AI by providing a scalable and potentially reliable evaluation method, reducing dependency on scarce human experts.
The validation process for medical AI systems could shift from predominantly human expert panels to a hybrid model including or led by LLM-based adjudication, speeding up innovation cycles.
- · AI developers
- · Healthcare AI companies
- · Patients in underserved regions
- · Traditional medical expert panels
- · Medical AI validation bottlenecks
LLMs effectively evaluate medical AI for diagnostic accuracy and clinical reasoning.
Faster, more cost-effective development and deployment of new medical AI systems, especially in resource-constrained settings.
LLMs could eventually participate directly in diagnostic reasoning and verification in clinical settings, augmenting human practitioners.
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Read at arXiv cs.AI