<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>29(5)</volume><submitter>Iannantuono GM</submitter><funding>National Cancer Institute</funding><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><funding>NIH HHS</funding><pubmed_abstract>&lt;h4>Background&lt;/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 patients with cancer and healthcare providers.&lt;h4>Materials and methods&lt;/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 4 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 30, 2023. Two reviewers evaluated the answers independently.&lt;h4>Results&lt;/h4>ChatGPT-4 and ChatGPT-3.5 answered all questions, whereas Google Bard answered </pubmed_abstract><journal>The oncologist</journal><pagination>407-414</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11067804</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Comparison of Large Language Models in Answering Immuno-Oncology Questions: A Cross-Sectional Study.</pubmed_title><pmcid>PMC11067804</pmcid><pubmed_authors>Choo-Wosoba H</pubmed_authors><pubmed_authors>Bracken-Clarke D</pubmed_authors><pubmed_authors>Gulley JL</pubmed_authors><pubmed_authors>Iannantuono GM</pubmed_authors><pubmed_authors>Floudas CS</pubmed_authors><pubmed_authors>Karzai F</pubmed_authors></additional><is_claimable>false</is_claimable><name>Comparison of Large Language Models in Answering Immuno-Oncology Questions: A Cross-Sectional Study.</name><description>&lt;h4>Background&lt;/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 patients with cancer and healthcare providers.&lt;h4>Materials and methods&lt;/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 4 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 30, 2023. Two reviewers evaluated the answers independently.&lt;h4>Results&lt;/h4>ChatGPT-4 and ChatGPT-3.5 answered all questions, whereas Google Bard answered </description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 May</publication><modification>2026-06-03T10:04:15.947Z</modification><creation>2026-04-26T03:11:36.948Z</creation></dates><accession>S-EPMC11067804</accession><cross_references><pubmed>38309720</pubmed><doi>10.1093/oncolo/oyae009</doi></cross_references></HashMap>