Cover not available

Article published In: Future Perspectives in Medical Argumentation
Edited by Sarah Bigi and Maria Grazia Rossi
[Journal of Argumentation in Context 14:3] 2025
► pp. 321–365

Get fulltext from our e-platform
JBE logo with link
EPUB logo with download linkDownload EPUB
authorization required

Large Language Models, argumentation, and healthcare

A socio-cognitive perspective

ORCiD logo with link | Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (ISTC-CNR)Roma
Published online: 4 December 2025
DOI logo with link
https://doi.org/10.1075/jaic.25024.pag
Abstract
There are various ways in which Large Language Models (LLMs), the latest breakthrough in Artificial Intelligence, are relevant for medicine: this paper focuses on their potential for supporting and improving argumentation in healthcare, both for patients and for practitioners. The message is mostly positive, suggesting adoption of such systems, but with specific cautions: most notably, the need to leverage them for enhancing human communicative and epistemic performance, rather than replacing it, and the importance of training users on few key principles to guide their deployment of LLMs in healthcare. The paper is accompanied by four concrete use cases, included in the supplementary materials, that constitute an integral and crucial part of this contribution.
Article outline
  • 1.Introduction
  • 2.Misplaced fears of AI for argumentation in healthcare
  • 3.How to improve patients’ information search and argumentative experience with LLMs
  • 4.How to hone argumentative skills in healthcare professionals using LLMs
  • 5.Conclusions
  • Notes
  • Supplementary materials
    • Case study 1. Patient’s use of LLMs to collect healthcare information on a mundane problem
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
    • Case study 2. Patient’s use of LLMs to collect healthcare information on a potentially serious problem
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
    • Case study 3. Healthcare worker’s use of LLMs to review and improve their argumentative strategies with patients
      • User
      • ChatGPT 4o-mini
    • Case study 4. Healthcare worker’s use of LLMs to roleplay an argumentative interaction with a problematic patient and receive feedback on performance
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
      • User
      • ChatGPT 4o-mini
  • References
References (70)
References
Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., Aziz, S., Damseh, R., Alrazak, S. A., & Sheikh, J. 2023. Large language models in medical education: opportunities, challenges, and future directions. JMIR Medical Education, 9(1), e48291. Google Scholar logo with link to Google Scholar
Acemoglu, D. 2002. Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7–72. Google Scholar logo with link to Google Scholar
Acemoglu, D., & Restrepo, P. 2018. The race between man and machine: implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. Google Scholar logo with link to Google Scholar
2019. Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. Google Scholar logo with link to Google Scholar
Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. 2023. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1–14. Google Scholar logo with link to Google Scholar
Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. 2021. Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1424. Google Scholar logo with link to Google Scholar
Arkoudas, K. 2023. ChatGPT is no stochastic parrot. But it also claims that 1 is greater than 1. Philosophy & Technology, 36(3), 54. Google Scholar logo with link to Google Scholar
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., & Smith, D. M. 2023. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine, 183(6), 589–596. Google Scholar logo with link to Google Scholar
Back, A. L., Fromme, E. K., & Meier, D. E. 2019. Training clinicians with communication skills needed to match medical treatments to patient values. Journal of the American Geriatrics Society, 67(S2), S435–S441. Google Scholar logo with link to Google Scholar
Becker, G., Kempf, D. E., Xander, C. J., Momm, F., Olschewski, M., & Blum, H. E. 2010. Four minutes for a patient, twenty seconds for a relative — an observational study at a university hospital. BMC Health Services Research, 101, 94. Google Scholar logo with link to Google Scholar
Bedi, S., Liu, Y., Orr-Ewing, L., Dash, D., Koyejo, S., Callahan, A., … & Shah, N. H. 2025. Testing and evaluation of health care applications of large language models: a systematic review. JAMA, 333(4), 319–328. Google Scholar logo with link to Google Scholar
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. 2021. On the dangers of stochastic parrots: Can language models be too big?🦜. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623). ACM. Google Scholar logo with link to Google Scholar
Bex, F., Grasso, F., Green, N., Paglieri, F., & Reed, C. (Eds.) 2017. Argument technologies: theory, analysis, and applications. College Publications.Google Scholar logo with link to Google Scholar
Broom, A. 2005. Virtually he@ lthy: the impact of internet use on disease experience and the doctor-patient relationship. Qualitative Health Research, 15(3), 325–345. Google Scholar logo with link to Google Scholar
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., … & Viale, R. 2024. The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus, 3(6), pgae 191. Google Scholar logo with link to Google Scholar
Caroprese, L., Vocaturo, E., & Zumpano, E. 2022. Argumentation approaches for explainable AI in medical informatics. Intelligent Systems with Applications, 161, 200109. Google Scholar logo with link to Google Scholar
Cassinadri, G. 2024. ChatGPT and the technology-education tension: Applying contextual virtue epistemology to a cognitive artifact. Philosophy & Technology, 37(1), 14. Google Scholar logo with link to Google Scholar
Chiu, Y. C. 2011. Probing, impelling, but not offending doctors: the role of the internet as an information source for patients’ interactions with doctors. Qualitative Health Research, 21(12), 1658–1666. Google Scholar logo with link to Google Scholar
Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J. N., Laleh, N. G., … & Kather, J. N. 2023. The future landscape of large language models in medicine. Communications Medicine, 3(1), 141. Google Scholar logo with link to Google Scholar
Costello, T. H., Pennycook, G., & Rand, D. G. 2024. Durably reducing conspiracy beliefs through dialogues with AI. Science, 385(6714), eadq1814. Google Scholar logo with link to Google Scholar
Čyras, K., Rago, A., Albini, E., Baroni, P., & Toni, F. 2021. Argumentative XAI: a survey. arXiv preprint arXiv:2105.11266.Google Scholar logo with link to Google Scholar
Dong, M., Conway, J. R., Bonnefon, J.-F., Shariff, A., & Rahwan, I. 2024. Fears about artificial intelligence across 20 countries and six domains of application. American Psychologist, advance online publication. Google Scholar logo with link to Google Scholar
Fanous, A., Goldberg, J., Agarwal, A. A., Lin, J., Zhou, A., Daneshjou, R., & Koyejo, S. 2025. SycEval: Evaluating LLM Sycophancy. arXiv preprint arXiv:2502.08177.Google Scholar logo with link to Google Scholar
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. 2021. The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750. Google Scholar logo with link to Google Scholar
Gottlieb, M., & Dyer, S. 2020. Information and disinformation: social media in the COVID-19 crisis. Academic Emergency Medicine, 27(7), 640–641. Google Scholar logo with link to Google Scholar
Granek, L., Krzyzanowska, M. K., Tozer, R., & Mazzotta, P. 2013. Oncologists’ strategies and barriers to effective communication about the end of life. Journal of Oncology Practice, 9(4), e129–e135. Google Scholar logo with link to Google Scholar
Hanemaayer, A. 2021. Don’t touch my stuff: historicising resistance to AI and algorithmic computer technologies in medicine. Interdisciplinary Science Reviews, 46(1–2), 126–137. Google Scholar logo with link to Google Scholar
Hart, A., Henwood, F., & Wyatt, S. 2004. The role of the Internet in patient-practitioner relationships: findings from a qualitative research study. Journal of Medical Internet Research, 6(3), e50. Google Scholar logo with link to Google Scholar
Higgins, T. S., Wu, A. W., Sharma, D., Illing, E. A., Rubel, K., Ting, J. Y., & Snot Force Alliance. 2020. Correlations of online search engine trends with coronavirus disease (COVID-19) incidence: infodemiology study. JMIR Public Health and Surveillance, 6(2), e19702. Google Scholar logo with link to Google Scholar
Johnson, R. H. 2000. Manifest rationality. A pragmatic theory of argument. Routledge.Google Scholar logo with link to Google Scholar
Jussupow, E., Spohrer, K., & Heinzl, A. 2022. Identity threats as a reason for resistance to artificial intelligence: survey study with medical students and professionals. JMIR Formative Research, 6(3), e28750. Google Scholar logo with link to Google Scholar
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … & Kasneci, G. 2023. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 1031, 102274. Google Scholar logo with link to Google Scholar
Knutzen, K. E., Sacks, O. A., Brody-Bizar, O. C., Murray, G. F., Jain, R. H., Holdcroft, L. A., … & Barnato, A. E. 2021. Actual and missed opportunities for end-of-life care discussions with oncology patients: a qualitative study. JAMA Network Open, 4(6), e2113193. Google Scholar logo with link to Google Scholar
Kökciyan, N., Sassoon, I. K., Young, A. P., Chapman, M. D., Porat, T. R., Ashworth, M., Curcin, V., Modgil, S., Parsons, S. D., & Sklar, E. I. 2018. Towards an argumentation system for supporting patients in self-managing their chronic conditions. In AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018, pp. 6–13), AAAI.Google Scholar logo with link to Google Scholar
Kreps, S., McCain, R. M., & Brundage, M. 2022. All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation. Journal of Experimental Political Science, 9(1), 104–117. Google Scholar logo with link to Google Scholar
Kurtz, S. M. 2002. Doctor-patient communication: principles and practices. Canadian Journal of Neurological Sciences, 29(S2), S23–S29. Google Scholar logo with link to Google Scholar
Lampos, V., Majumder, M. S., Yom-Tov, E., Edelstein, M., Moura, S., Hamada, Y., Rangaka, M. X., McKendry, R. A., & Cox, I. J. 2021. Tracking COVID-19 using online search. NPJ Digital Medicine, 4(1), 17. Google Scholar logo with link to Google Scholar
Lawrence, J., & Reed, C. 2020. Argument mining: a survey. Computational Linguistics, 45(4), 765–818. Google Scholar logo with link to Google Scholar
Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. 2022. The medical algorithmic audit. The Lancet Digital Health, 4(5), e384–e397. Google Scholar logo with link to Google Scholar
Longoni, C., Bonezzi, A., & Morewedge, C. K. 2019. Resistance to medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. Google Scholar logo with link to Google Scholar
Maaz, S., Palaganas, J. C., Palaganas, G., & Bajwa, M. 2025. A guide to prompt design: foundations and applications for healthcare simulationists. Frontiers in Medicine, 111, 1504532. Google Scholar logo with link to Google Scholar
Maleki, N., Padmanabhan, B., & Dutta, K. 2024. AI hallucinations: a misnomer worth clarifying. In Proceedings of the 2024 IEEE Conference on Artificial Intelligence (CAI) (pp. 133–138). IEEE. Google Scholar logo with link to Google Scholar
Mayer, T., Cabrio, E., & Villata, S. 2020. Transformer-based argument mining for healthcare applications. In G. De Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarín, & J. Lang (Eds.), Proceedings of the 24th European Conference on Artificial Intelligence, ECAI 2020 (pp. 2108–2115). IOS Press.Google Scholar logo with link to Google Scholar
Menz, B. D., Modi, N. D., Sorich, M. J., & Hopkins, A. M. 2024. Health disinformation use case highlighting the urgent need for artificial intelligence vigilance: weapons of mass disinformation. JAMA Internal Medicine, 184(1), 92–96. Google Scholar logo with link to Google Scholar
Meskó, B., & Topol, E. J. 2023. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digital Medicine, 6(1), 120. Google Scholar logo with link to Google Scholar
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. 2022. Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 551, 3503–3568. Google Scholar logo with link to Google Scholar
Moreira, M. W., Rodrigues, J. J., Korotaev, V., Al-Muhtadi, J., & Kumar, N. 2019. A comprehensive review on smart decision support systems for health care. IEEE Systems Journal, 13(3), 3536–3545. Google Scholar logo with link to Google Scholar
Musi, E., Kökciyan, N., Al Khatib, K., Ceolin, D., Dietz, E., Gutekunst, K., Hautli-Janisz, A., Santibáñez, C., Schneider, J., Scholz, J., Steging, C., Visser, J., & Wachsmuth, H. 2025. Toward reasonable parrots: why Large Language Models should argue with us by design. In Proceedings of the 12th Argument Mining Workshop (pp. 24–31), ACL. Google Scholar logo with link to Google Scholar
Noy, S., & Zhang, W. 2023. Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. Google Scholar logo with link to Google Scholar
O’Neil, C. 2016. Weapons of math destruction. Crown Books.Google Scholar logo with link to Google Scholar
Paglieri, F. 2024. Expropriated minds: on some practical problems of generative AI, beyond our cognitive illusions. Philosophy & Technology, 37(2), 1–30. Google Scholar logo with link to Google Scholar
Piantadosi, S. T., & Hill, F. 2022. Meaning without reference in large language models. arXiv preprint arXiv:2208.02957.Google Scholar logo with link to Google Scholar
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. 2022. AI in health and medicine. Nature Medicine, 28(1), 31–38. Google Scholar logo with link to Google Scholar
Ray, P. P. 2024. Can LLMs improve existing scenario of healthcare?. Journal of Hepatology, 80(1), e28–e29. Google Scholar logo with link to Google Scholar
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., … & Bengio, Y. 2022. Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 42.Google Scholar logo with link to Google Scholar
Silver, M. P. 2015. Patient perspectives on online health information and communication with doctors: a qualitative study of patients 50 years old and over. Journal of Medical Internet Research, 17(1), e3588. Google Scholar logo with link to Google Scholar
Song, X., Xu, B., & Zhao, Z. 2022. Can people experience romantic love for artificial intelligence? An empirical study of intelligent assistants. Information & Management, 59(2), 103595. Google Scholar logo with link to Google Scholar
Stevenson, F. A., Kerr, C., Murray, E., & Nazareth, I. 2007. Information from the Internet and the doctor-patient relationship: the patient perspective–a qualitative study. BMC Family Practice, 81, 47. Google Scholar logo with link to Google Scholar
Swire-Thompson, B., & Lazer, D. 2020. Public health and online misinformation: challenges and recommendations. Annual Review of Public Health, 41(1), 433–451. Google Scholar logo with link to Google Scholar
Tan, S. S. L., & Goonawardene, N. 2017. Internet health information seeking and the patient-physician relationship: a systematic review. Journal of Medical Internet Research, 19(1), e9. Google Scholar logo with link to Google Scholar
Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S. 2024. Large language models streamline automated machine learning for clinical studies. Nature Communications, 15(1), 1603. Google Scholar logo with link to Google Scholar
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. 2023. Large language models in medicine. Nature Medicine, 29(8), 1930–1940. Google Scholar logo with link to Google Scholar
Topol, E. J. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. Google Scholar logo with link to Google Scholar
Vassiliades, A., Bassiliades, N., & Patkos, T. 2021. Argumentation and explainable artificial intelligence: a survey. The Knowledge Engineering Review, 361, e5. Google Scholar logo with link to Google Scholar
Walters, W. H., & Wilder, E. I. 2023. Fabrication and errors in the bibliographic citations generated by ChatGPT. Scientific Reports, 13(1), 14045. Google Scholar logo with link to Google Scholar
Wang, D., & Zhang, S. 2024. Large language models in medical and healthcare fields: applications, advances, and challenges. Artificial Intelligence Review, 57(11), 299. Google Scholar logo with link to Google Scholar
Wang, L., Wan, Z., Ni, C., Song, Q., Li, Y., Clayton, E., Malin, B., & Yin, Z. 2024. Applications and concerns of ChatGPT and other conversational Large Language Models in health care: systematic review. Journal of Medical Internet Research, 261, e22769. Google Scholar logo with link to Google Scholar
Yang, Y., & Ngai, E. W. 2024. The influence of physician stance on patient resistance to healthcare AI. In Proceedings of AMCIS 2024 (vol. 41, pp. 2656–2660). Association for Information Systems (AIS).Google Scholar logo with link to Google Scholar
Yang, Y., Ngai, E. W., & Wang, L. 2024. Resistance to artificial intelligence in health care: literature review, conceptual framework, and research agenda. Information & Management, 103961. Google Scholar logo with link to Google Scholar
Zada, T., Tam, N., Barnard, F., Van Sittert, M., Bhat, V., & Rambhatla, S. 2025. Medical misinformation in AI-assisted self-diagnosis: development of a method (EvalPrompt) for analyzing Large Language Models. JMIR Formative Research, 9(1), e66207. Google Scholar logo with link to Google Scholar
Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue