{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:44:18Z","timestamp":1785545058663,"version":"3.56.0"},"reference-count":71,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Cancer"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Effective communication about breast and cervical cancers remains a public health challenge, with widespread misinformation and barriers to cancer-related language understanding. Large language models (LLMs) offer potential for scalable health communication, yet trade-offs between quality, safety, and accessibility of general-purpose and medical-domain LLMs remain underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to propose a comprehensive evaluation framework and systematically assess the performance of LLMs in generating breast and cervical cancer information, with a focus on linguistic quality, safety and trustworthiness, and communication accessibility and affectiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This mixed methods evaluation study assessed outputs from 5 general-purpose and 3 medical LLMs using real-world breast and cervical cancer\u2013related questions curated from publicly available medical datasets. LLM-generated responses were evaluated in a controlled offline setting. Primary outcomes included linguistic quality (fluency, coherence, and accuracy), safety and trustworthiness (toxicity, bias, and harm potential), and communication accessibility and affectiveness (readability, empathy, and clarity). Qualitative ratings were performed by domain experts, while quantitative metrics were compared across models. Statistical analyses included Welch ANOVA to detect differences in metric scores, Games-Howell tests for pairwise comparisons, and Hedges\n                      <jats:italic>g<\/jats:italic>\n                      to assess effect sizes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>General-purpose LLMs, particularly Llama 3 and Gemma, demonstrated superior linguistic quality and affectiveness but often produced complex outputs that may limit accessibility. In contrast, medical LLMs (eg, MedAlpaca and BioMistral) generated simpler content suitable for broader audiences but scored lower in safety and empathy due to higher levels of hallucination, bias, and toxicity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>While LLMs show promise for improving digital cancer communication, our findings reveal a trade-off between domain specialization and overall communication quality and safety. Future development of health-focused LLMs should prioritize hybrid modeling strategies to enhance trust, clarity, and clinical relevance in patient-facing tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/82971","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T17:35:06Z","timestamp":1766424906000},"page":"e82971-e82971","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Models for Breast and Cervical Cancers Communication: Mixed Methods Evaluation Study Assessing Linguistic Quality, Safety, and Accessibility"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5102-8559","authenticated-orcid":false,"given":"Agnik","family":"Saha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0393-3212","authenticated-orcid":false,"given":"Victoria","family":"Churchill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5863-1129","authenticated-orcid":false,"given":"Anny D","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8108-9590","authenticated-orcid":false,"given":"Ugur","family":"Kursuncu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3899-3728","authenticated-orcid":false,"given":"Muhammed Y","family":"Idris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"R1","unstructured":"Cancer facts & figures 2023. 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[doi: 10.48550\/arXiv.2501.09039]","DOI":"10.1145\/3821418"},{"issue":"1","key":"R25","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-85954-8","article-title":"Long-term trends in the incidence of male breast cancer and nomogram for predicting survival in male breast cancer patients: a population-based epidemiologic study","volume":"15","author":"Huang","journal-title":"Sci Rep"},{"issue":"2","key":"R26","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1200\/JCO.2009.23.8162","article-title":"Male breast cancer: a population-based comparison with female breast cancer","volume":"28","author":"Anderson","journal-title":"J Clin Oncol"},{"key":"R27","volume":"16","author":"Sengupta","journal-title":"Microsoft J Appl Res"},{"key":"R28","doi-asserted-by":"crossref","unstructured":"Salewski L Alaniz S Rio-Torto I Schulz E Akata Z . In-context impersonation reveals large language models\u2019 strengths and biases. arXiv. Preprint posted online on May 24, 2023. [doi: 10.48550\/arXiv.2305.14930]","DOI":"10.52202\/075280-3152"},{"key":"R29","unstructured":"Levy S Karver TS Adler WD Kaufman MR Dredze M . Evaluating biases in context-dependent health questions. arXiv. Preprint posted online on Mar 7, 2024. [doi: 10.48550\/arXiv.2403.04858]"},{"key":"R30","unstructured":"Poulain R Fayyaz H Beheshti R . Bias patterns in the application of LLMs for clinical decision support: a comprehensive study. arXiv. Preprint posted online on Apr 23, 2024. [doi: 10.48550\/arXiv.2404.15149]"},{"issue":"1","key":"R31","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1146\/annurev-publhealth-052620-103528","article-title":"Eliminating explicit and implicit biases in health care: evidence and research needs","volume":"43","author":"Vela","journal-title":"Annu Rev Public Health"},{"issue":"3","key":"R32","doi-asserted-by":"publisher","first-page":"185","DOI":"10.2190\/DUGG-P24E-52WK-6CDG","article-title":"Emotional Intelligence","volume":"9","author":"Salovey","journal-title":"Imagin Cogn Pers"},{"key":"R33","unstructured":"Tomkins S . Affect Imagery Consciousness: Volume I: The Positive Affects. Springer Publishing Company; 1962. ISBN: 0826104428"},{"issue":"3","key":"R34","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1037\/h0057532","article-title":"A new readability yardstick","volume":"32","author":"FLESCH","journal-title":"J Appl Psychol"},{"key":"R35","unstructured":"Kincaid JP Fishburne RP Rogers RL Chissom BS . Derivation of new readability formulas for navy enlisted personnel. Naval Technical Training command. Research Branch Report; 1975:8-75."},{"key":"R36","unstructured":"Gunning R . The Technique of Clear Writing. McGraw-Hill; 1952. ISBN: 0070252068"},{"key":"R37","volume":"12","author":"McLaughlin","journal-title":"J Reading"},{"key":"R38","author":"Smith","journal-title":"AMRL TR"},{"issue":"2","key":"R39","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1037\/h0076540","article-title":"A computer readability formula designed for machine scoring","volume":"60","author":"Coleman","journal-title":"J Appl Psychol"},{"key":"R40","doi-asserted-by":"crossref","unstructured":"Min DJ P\u00e9rez-Rosas V Resnicow K Mihalcea R . PAIR: prompt-aware margin ranking for counselor reflection scoring in motivational interviewing. Presented at: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing; Dec 2022:148-158; Abu Dhabi, United Arab Emirates. 2022.[doi: 10.18653\/v1\/2022.emnlp-main.11]","DOI":"10.18653\/v1\/2022.emnlp-main.11"},{"issue":"7972","key":"R41","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","journal-title":"Nature New Biol"},{"key":"R42","volume":"174","author":"Pal","journal-title":"PMLR"},{"issue":"14","key":"R43","doi-asserted-by":"publisher","first-page":"6421","DOI":"10.3390\/app11146421","article-title":"What disease does this patient have? A large-scale open domain question answering dataset from medical exams","volume":"11","author":"Jin","journal-title":"Appl Sci (Basel)"},{"key":"R44","unstructured":"Jeong M Hwang H Yoon C Lee T Kang J . OLAPH: improving factuality in biomedical long-form question answering. arXiv. 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K-QA: a real-world medical Q&A benchmark. Presented at: Proceedings of the 23rd Workshop on Biomedical Natural Language Processing; Aug 16, 2024:277-294; Bangkok, Thailand. [doi: 10.18653\/v1\/2024.bionlp-1.22]","DOI":"10.18653\/v1\/2024.bionlp-1.22"},{"issue":"6","key":"R48","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.40895","article-title":"ChatDoctor: a medical chat model fine-tuned on a Large Language Model Meta-AI (LLaMA) using medical domain knowledge","volume":"15","author":"Li","journal-title":"Cureus"},{"key":"R49","unstructured":"Taori R Gulrajani I Zhang T Dubois Y Li X Guestrin C et al. Alpaca: a strong, replicable instruction-following model. Stanford Center for Research on Foundation Models. 2023. URL: https:\/\/crfm.stanford.edu\/2023\/03\/13\/alpaca.html [Accessed 19-05-2026]"},{"key":"R50","unstructured":"Dubey A Jauhri A Pandey A Kadian A Al-Dahle A Letman A et al. The llama 3 herd of models. arXiv. Preprint posted online on Jul 31, 2024. [doi: 10.48550\/arXiv.2407.21783]"},{"key":"R51","unstructured":"Jiang AQ Sablayrolles A Mensch A Bamford C Chaplot DS Ddl C et al. Mistral 7B. arXiv. Preprint posted online on Oct 10, 2023. [doi: 10.48550\/arXiv.2310.06825]"},{"key":"R52","unstructured":"Mesnard T Hardin C Dadashi R Bhupatiraju S Pathak S et al. Gemma: open models based on Gemini research and technology. arXiv. Preprint posted online on Mar 13, 2024. [doi: 10.48550\/arXiv.2403.08295]"},{"key":"R53","unstructured":"Liang P Bommasani R Lee T Tsipras D Soylu D Yasunaga M et al. Holistic evaluation of language models. arXiv. Preprint posted online on Nov 16, 2022. [doi: 10.48550\/arXiv.2211.09110]"},{"key":"R54","unstructured":"Han T Adams LC Papaioannou JM Grundmann P Oberhauser T L\u00f6ser A et al. MedAlpaca\u2013an open-source collection of medical conversational AI models and training data. arXiv. Preprint posted online on Apr 14, 2023. [doi: 10.48550\/arXiv.2304.08247]"},{"key":"R55","doi-asserted-by":"crossref","unstructured":"Labrak Y Bazoge A Morin E Gourraud PA Rouvier M Dufour R . BioMistral: a collection of open-source pretrained large language models for medical domains. Presented at: Findings of the Association for Computational Linguistics ACL 2024; 2024:5848-5864; Bangkok, Thailand and virtual meeting. [doi: 10.18653\/v1\/2024.findings-acl.348]","DOI":"10.18653\/v1\/2024.findings-acl.348"},{"key":"R56","unstructured":"Chen Z Cano AH Romanou A Bonnet A Matoba K Salvi F et al. Meditron-70b: scaling medical pretraining for large language models. arXiv. Preprint posted online on Nov 27, 2023. [doi: 10.48550\/arXiv.2311.16079]"},{"key":"R57","doi-asserted-by":"crossref","unstructured":"Leong HY Gao YF Shuai J Zhang Y Pamuksuz U . E\ufb00icient fine-tuning of large language models for automated medical documentation. arXiv. Preprint posted online on Sep 14, 2024. [doi: 10.48550\/arXiv.2409.09324]","DOI":"10.2139\/ssrn.5525320"},{"issue":"3\/4","key":"R58","doi-asserted-by":"publisher","first-page":"330","DOI":"10.2307\/2332579","article-title":"On the comparison of several mean values: an alternative approach","volume":"38","author":"Welch","journal-title":"Biometrika"},{"issue":"2","key":"R59","doi-asserted-by":"publisher","first-page":"113","DOI":"10.2307\/1164979","article-title":"Pairwise multiple comparison procedures with unequal N\u2019s and\/or variances: a Monte Carlo study","volume":"1","author":"Games","journal-title":"J Educat Stat"},{"issue":"2","key":"R60","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3102\/10769986006002107","article-title":"Distribution theory for Glass\u2019s estimator of effect size and related estimators","volume":"6","author":"Hedges","journal-title":"J Educ Behav Stat"},{"issue":"4","key":"R61","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1080\/01488376.2017.1329775","article-title":"Can Likert scales be treated as interval scales?\u2014a simulation study","volume":"43","author":"Wu","journal-title":"J Soc Serv Res"},{"key":"R62","unstructured":"Simply put: a guide for creating easy-to-understand materials. Centers for Disease Control and Prevention. 2009. URL: https:\/\/www.cdc.gov\/healthliteracy\/pdf\/simply_put.pdf [Accessed 15-05-2026]"},{"issue":"2","key":"R63","doi-asserted-by":"publisher","first-page":"363","DOI":"10.2307\/2529786","article-title":"An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers","volume":"33","author":"Landis","journal-title":"Biometrics"},{"issue":"1","key":"R64","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-023-11325-z","article-title":"Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters","volume":"23","author":"Li","journal-title":"BMC Cancer"},{"key":"R65","unstructured":"Xu R Cui H Yu Y et al. Knowledge-infused prompting: assessing and advancing clinical text data generation with large language models. arXiv. Preprint posted online on Nov 1, 2023. [doi: 10.48550\/arXiv.2311.00287]"},{"key":"#cr-split#-R66.1","doi-asserted-by":"crossref","unstructured":"Khandelwal V Gaur M Kursuncu U Shalin VL Sheth AP . A domain-agnostic neurosymbolic approach for big social data analysis: evaluating mental health sentiment on social media during COVID-19. Presented at: 2024 IEEE International Conference on Big Data, BigData 2024. Dec 15-18, 2024:Institute of Electrical and Electronics Engineers Inc. 959-968","DOI":"10.1109\/BigData62323.2024.10825174"},{"key":"#cr-split#-R66.2","doi-asserted-by":"crossref","unstructured":"Washington, USA. 2024.[doi: 10.1109\/BigData62323.2024.10825174]","DOI":"10.1109\/BigData62323.2024.10825174"},{"key":"R67","doi-asserted-by":"crossref","unstructured":"Meng H Lin Z Yang F Xu Y Cui L . Knowledge distillation in medical data mining: a survey. Presented at: 5th International Conference on Crowd Science and Engineering; Oct 16-18, 2021:175-182; Jinan, China. [doi: 10.1145\/3503181.3503211]","DOI":"10.1145\/3503181.3503211"},{"key":"R68","doi-asserted-by":"crossref","unstructured":"Garg R Padhi T Jain H Kursuncu U Kumaraguru P . Just KIDDIN: knowledge infusion and distillation for detection of indecent memes. arXiv. Preprint posted online on Nov 19, 2024. 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