ChatGPT compared to national guidelines for management of ovarian cancer: Did ChatGPT get it right? – A Memorial Sloan Kettering Cancer Center Team Ovary study Journal Article
| Authors: | Finch, L.; Broach, V.; Feinberg, J.; Al-Niaimi, A.; Abu-Rustum, N. R.; Zhou, Q.; Iasonos, A.; Chi, D. S. |
| Article Title: | ChatGPT compared to national guidelines for management of ovarian cancer: Did ChatGPT get it right? – A Memorial Sloan Kettering Cancer Center Team Ovary study |
| Abstract: | Objectives: We evaluated the performance of a chatbot compared to the National Comprehensive Cancer Network (NCCN) Guidelines for the management of ovarian cancer. Methods: Using NCCN Guidelines, we generated 10 questions and answers regarding management of ovarian cancer at a single point in time. Questions were thematically divided into risk factors, surgical management, medical management, and surveillance. We asked ChatGPT (GPT-4) to provide responses without prompting (unprompted GPT) and with prompt engineering (prompted GPT). Responses were blinded and evaluated for accuracy and completeness by 5 gynecologic oncologists. A score of 0 was defined as inaccurate, 1 as accurate and incomplete, and 2 as accurate and complete. Evaluations were compared among NCCN, unprompted GPT, and prompted GPT answers. Results: Overall, 48% of responses from NCCN, 64% from unprompted GPT, and 66% from prompted GPT were accurate and complete. The percentage of accurate but incomplete responses was higher for NCCN vs GPT-4. The percentage of accurate and complete scores for questions regarding risk factors, surgical management, and surveillance was higher for GPT-4 vs NCCN; however, for questions regarding medical management, the percentage was lower for GPT-4 vs NCCN. Overall, 14% of responses from unprompted GPT, 12% from prompted GPT, and 10% from NCCN were inaccurate. Conclusions: GPT-4 provided accurate and complete responses at a single point in time to a limited set of questions regarding ovarian cancer, with best performance in areas of risk factors, surgical management, and surveillance. Occasional inaccuracies, however, should limit unsupervised use of chatbots at this time. © 2024 Elsevier Inc. |
| Keywords: | ovarian cancer; artificial intelligence; large language models |
| Journal Title: | Gynecologic Oncology |
| Volume: | 189 |
| ISSN: | 0090-8258 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2024-10-01 |
| Start Page: | 75 |
| End Page: | 79 |
| Language: | English |
| DOI: | 10.1016/j.ygyno.2024.07.007 |
| PROVIDER: | scopus |
| PUBMED: | 39042956 |
| PMCID: | PMC11402584 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Dennis S. Chi -- Source: Scopus |
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