From raw audio to structure: an agent-based pipeline that boosts medical LLM performance.

Multi-agent pipeline converting raw clinical audio recordings into structured high-quality data to boost medical LLM performance

Medical LLMs depend on high-quality training corpora, often degraded by noise, transcription errors, and speaker overlap in clinical recordings. This multi-agent pipeline automatically restructures raw doctor-patient audio recordings into high-quality structured data. It significantly improves LLM performance on standardized clinical tasks, with validation on real consultation data. The dataset and code are published open-source.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

The 'garbage in, garbage out' principle applied to medical LLMs is well illustrated: without prior structuring of conversational data, even the best models fail. Direct applicability to clinical genomics is limited, but the principle is transferable to automated phenotyping workflows and genetics consultations.

Clinical impact: 3/3 · Evidence strength: 3/3 · Novelty: 2/2 · Sample size: 1/1 · Publication status: 1/1 → Total: 10/10