Multi‑agent AI workflow proposed to generate live summaries for thoracic tumor boards

Overview

A new preprint on arXiv describes the development, evaluation and deployment of a multi‑agent system intended to produce concise, live patient summaries for thoracic tumor boards. Tumor boards are multidisciplinary conferences—radiology, pathology, oncology and surgery—where fast, accurate case synthesis drives treatment decisions. The authors present a manual AI‑assisted workflow that aggregates primary radiology and pathology data into on‑screen summaries to support real‑time discussion.

Development and reported evaluation

The paper describes a multi‑agent architecture that the authors say organizes specialized processing steps to extract and condense imaging, pathology and chart information into succinct case briefs. The team reports both qualitative and quantitative assessments from pilot use, and it has been reported that deployment in clinical meetings reduced time spent presenting cases and improved perceived clarity of recommendations. Reportedly, the system remains human‑in‑the‑loop: clinicians verify and edit summaries before they are used for decision‑making.

Context, risks and next steps

AI assistance for tumor boards touches familiar issues: clinical safety, explainability, data governance and regulatory approval. Can automated summaries scale without introducing errors? Independent validation, prospective trials and clear audit trails will be necessary before such tools can be widely adopted. The preprint is hosted on arXiv, where arXivLabs supports community development and sharing of research; interested readers can consult the full paper for methodological details and the authors’ evaluation data.