Towards conversational diagnostic artificial intelligence.

ABSTRACT: At the heart of medicine lies physician-patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust^1,2. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians' expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self-play-based³ simulated environment with automated feedback for scaling learning across disease conditions, specialties and contexts. We designed a framework for evaluating clinically meaningful axes of performance, including history-taking, diagnostic accuracy, management

SUBMITTER: Tu T 

PROVIDER: S-EPMC12158756 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature