Dataset Information

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

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 <0.0001). The number of questions with reproducible answers was higher for ChatGPT-4 (95%) and ChatGPT3.5 (88.3%) than for Google Bard (50%) (p <0.0001). In terms of accuracy, the number of answers deemed fully correct were 75.4%, 58.5%, and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (p = 0.03). Furthermore, the number of responses deemed highly relevant was 71.9%, 77.4%, and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (p = 0.04). Regarding readability, the number of highly readable was higher for ChatGPT-4 and ChatGPT-3.5 (98.1%) and (100%) compared to Google Bard (87.5%) (p = 0.02).

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.

SUBMITTER: Iannantuono GM

PROVIDER: S-EPMC10705618 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Iannantuono Giovanni Maria GM Bracken-Clarke Dara D Karzai Fatima F Choo-Wosoba Hyoyoung H Gulley James L JL Floudas Charalampos S CS

medRxiv : the preprint server for health sciences 20231031

<h4>Background</h4>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.<h4>Materials and methods</h4>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 ...[more]

PMID: 38076813

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Dataset Information

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

Background

Materials and methods

Results

Conclusion

Publications

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

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