Comparison of Large Language Models in Answering Immuno-Oncology Questions: A Cross-Sectional Study

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Abstract
Background The capability of large language models (LLMs) to understand and generate human-readable text has prompted the investigation of their potential as educational and management tools for cancer patients and healthcare providers. Materials and Methods We conducted a cross-sectional study aimed at evaluating the ability of ChatGPT-4, ChatGPT-3.5, and Google Bard to answer questions related to four domains of immuno-oncology (Mechanisms, Indications, Toxicities, and Prognosis). We generated 60 open-ended questions (15 for each section). Questions were manually submitted to LLMs, and responses were collected on June 30th, 2023. Two reviewers evaluated the answers independently. Results ChatGPT-4 and ChatGPT-3.5 answered all questions, whereas Google Bard answered only 53.3% (p Conclusion ChatGPT-4 and ChatGPT-3.5 are potentially powerful tools in immuno-oncology, whereas Google Bard demonstrated relatively poorer performance. However, the risk of inaccuracy or incompleteness in the responses was evident in all three LLMs, highlighting the importance of expert-driven verification of the outputs returned by these technologies. IMPLICATIONS FOR PRACTICE Several studies have recently evaluated whether large language models may be feasible tools for providing educational and management information for cancer patients and healthcare providers. In this cross-sectional study, we assessed the ability of ChatGPT-4, ChatGPT-3.5, and Google Bard to answer questions related to immuno-oncology. ChatGPT-4 and ChatGPT-3.5 returned a higher proportion of responses, which were more accurate and comprehensive, than those returned by Google Bard, yielding highly reproducible and readable outputs. These data support ChatGPT-4 and ChatGPT-3.5 as powerful tools in providing information on immuno-oncology; however, accuracy remains a concern, with expert assessment of the output still indicated.
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