What question did this study set out to answer?

The aim is to evaluate how well a large language model performs in generating recommendations for soft tissue sarcoma tumor boards.

March 1, 2026Open Access

When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions

Key Points

The aim is to evaluate how well a large language model performs in generating recommendations for soft tissue sarcoma tumor boards.
Utilized ChatGPT-4o for generating treatment recommendations.
Rated performance based on expert evaluations.
Analyzed discrepancies in treatment sequencing and chemotherapy selection.
ChatGPT-4o achieved high expert-rated performance in recommendations.
Excels at personalizing contextualization of cases.
Identified discrepancies indicate a need for expert oversight.

Abstract

ChatGPT-4o demonstrated substantial expert-rated performance in generating tumor board recommendations for soft tissue sarcoma cases, particularly excelling in personalized contextualization. Discrepancies in treatment sequencing and chemotherapy selection highlight the need for expert oversight. These findings support the feasibility of LLM integration into oncology workflows, warranting further refinement toward safe, supportive clinical use.

When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions

Key Points

Abstract

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