{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T12:58:03Z","timestamp":1768309083695,"version":"3.49.0"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The integration of large language models (LLMs) into cardio-oncology patient education holds promise for addressing the critical gap in accessible, accurate, and patient-friendly information. However, the performance of publicly available LLMs in this specialized domain remains underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study evaluates the performance of three LLMs (ChatGPT-4, Kimi, DouBao) act as assistants for physicians in cardio-oncology patient education and examines the impact of prompt engineering on response quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Twenty standardized questions spanning cardio-oncology topics were posed twice to three LLMs (ChatGPT-4, Kimi, DouBao): once without prompts and once with a directive to simplify language, generating 240 responses. These responses were evaluated by four cardio-oncology specialists for accuracy, comprehensiveness, helpfulness, and practicality. Readability and complexity were assessed using a Chinese text analysis framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 240 responses, 63.3% were rated \u201ccorrect,\u201d 35.0% \u201cpartially correct,\u201d and 1.7% \u201cincorrect.\u201d No significant differences in accuracy were observed between models (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.26). Kimi demonstrated no incorrect responses. Significant declines in comprehensiveness (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.03) and helpfulness (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01) occurred post-prompt, particularly for DouBao (accuracy: 57.5% vs. 7.5%,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01). Readability metrics (readability age, difficulty score, total word count, sentence length) showed no inter-model differences, but prompts reduced complexity (e.g., DouBao\u2019s readability age decreased from 12.9\u202f\u00b1\u202f0.8 to 10.1\u202f\u00b1\u202f1.2\u202fyears,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Publicly available LLMs provide largely accurate responses to cardio-oncology questions, yet their utility is constrained by inconsistent comprehensiveness and sensitivity to prompt design. While simplifying language improves readability, it risks compromising clinical relevance. Tailored fine-tuning and specialized evaluation frameworks are essential to optimize LLMs for patient education in cardio-oncology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1693446","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:35:00Z","timestamp":1768286100000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the efficacy of large language models in cardio-oncology patient education: a comparative analysis of accuracy, readability, and prompt engineering strategies"],"prefix":"10.3389","volume":"8","author":[{"given":"Zhao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiran","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojie","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"107874","DOI":"10.1016\/j.compbiomed.2023.107874","article-title":"Enhancing patient education in cancer care: intelligent cancer patient education model for effective communication","volume":"169","author":"An","year":"2024","journal-title":"Comput. 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