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, 2026Open Access

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

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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Discussion

Cite this study

Buonaiuto et al. (Sun,) studied this question.

www.synapsesocial.com/papers/69ada885bc08abd80d5bb7da — DOI: https://doi.org/10.1200/cci-25-00230

Authors

R. Buonaiuto

Aldo Caltavituro

Rossana Di Rienzo

Journals

JCO Clinical Cancer Informatics

Actions

Institutions

Harvard University

University of Chicago

Inserm

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

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

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Cite this study

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion