What question did this study set out to answer?

To evaluate the decision-making capabilities of a large language model in early breast cancer treatment recommendations.

March 8, 2026JCO Clinical Cancer Informatics0 citationsOpen Access

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Analysis of Large Language Model Decision Making in Hormone Receptor–Positive/Human Epidermal Growth Factor Receptor 2–Negative Early Breast Cancer

RBR. BuonaiutoIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale"ACAldo CaltavituroFederico II University Hospital RRRossana Di RienzoIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale"

Key Points

To evaluate the decision-making capabilities of a large language model in early breast cancer treatment recommendations.
Compared GPT-4o recommendations with clinician opinions pretest and post-test.
Assessed the accuracy of multigene testing recommendations.
Analyzed the agreement levels between clinicians and the model.
Modest pretest agreement between clinicians and GPT-4o.
Post-test agreement significantly improved.
Highlights the potential role of AI in enhancing clinical decision-making.

Abstract

The agreement between clinicians and GPT-4o in pretest recommendations was modest but improved post-test, highlighting the importance of multigene testing and the potential of large language models in clinical decision making.

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Analysis of Large Language Model Decision Making in Hormone Receptor–Positive/Human Epidermal Growth Factor Receptor 2–Negative Early Breast Cancer

Key Points

Abstract

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