1887
Volume 14, Issue 3
  • ISSN 2211-4742
  • E-ISSN: 2211-4750
USD
Buy:$35.00 + Taxes

Abstract

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.

Loading

Article metrics loading...

/content/journals/10.1075/jaic.25024.pag
2025-12-04
2026-01-13
Loading full text...

Full text loading...

References

  1. 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. 10.2196/48291
    https://doi.org/10.2196/48291 [Google Scholar]
  2. Acemoglu, D.
    2002 Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7–72. 10.1257/jel.40.1.7
    https://doi.org/10.1257/jel.40.1.7 [Google Scholar]
  3. 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. 10.1257/aer.20160696
    https://doi.org/10.1257/aer.20160696 [Google Scholar]
  4. 2019 Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. 10.1257/jep.33.2.3
    https://doi.org/10.1257/jep.33.2.3 [Google Scholar]
  5. 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. 10.1057/s41599‑023‑01787‑8
    https://doi.org/10.1057/s41599-023-01787-8 [Google Scholar]
  6. 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. 10.1002/widm.1424
    https://doi.org/10.1002/widm.1424 [Google Scholar]
  7. Arkoudas, K.
    2023 ChatGPT is no stochastic parrot. But it also claims that 1 is greater than 1. Philosophy & Technology, 36(3), 54. 10.1007/s13347‑023‑00619‑6
    https://doi.org/10.1007/s13347-023-00619-6 [Google Scholar]
  8. 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. 10.1001/jamainternmed.2023.1838
    https://doi.org/10.1001/jamainternmed.2023.1838 [Google Scholar]
  9. 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. 10.1111/jgs.15709
    https://doi.org/10.1111/jgs.15709 [Google Scholar]
  10. 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. 10.1186/1472‑6963‑10‑94
    https://doi.org/10.1186/1472-6963-10-94 [Google Scholar]
  11. 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. 10.1001/jama.2024.21700
    https://doi.org/10.1001/jama.2024.21700 [Google Scholar]
  12. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S.
    2021 On the dangers of stochastic parrots: Can language models be too big?🦜. InProceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623). ACM. 10.1145/3442188.3445922
    https://doi.org/10.1145/3442188.3445922 [Google Scholar]
  13. Bex, F., Grasso, F., Green, N., Paglieri, F., & Reed, C.
    (Eds.) 2017Argument technologies: theory, analysis, and applications. College Publications.
    [Google Scholar]
  14. 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. 10.1177/1049732304272916
    https://doi.org/10.1177/1049732304272916 [Google Scholar]
  15. 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), pgae191. 10.1093/pnasnexus/pgae191
    https://doi.org/10.1093/pnasnexus/pgae191 [Google Scholar]
  16. Caroprese, L., Vocaturo, E., & Zumpano, E.
    2022 Argumentation approaches for explainable AI in medical informatics. Intelligent Systems with Applications, 161, 200109. 10.1016/j.iswa.2022.200109
    https://doi.org/10.1016/j.iswa.2022.200109 [Google Scholar]
  17. Cassinadri, G.
    2024 ChatGPT and the technology-education tension: Applying contextual virtue epistemology to a cognitive artifact. Philosophy & Technology, 37(1), 14. 10.1007/s13347‑024‑00701‑7
    https://doi.org/10.1007/s13347-024-00701-7 [Google Scholar]
  18. 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. 10.1177/1049732311417455
    https://doi.org/10.1177/1049732311417455 [Google Scholar]
  19. 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. 10.1038/s43856‑023‑00370‑1
    https://doi.org/10.1038/s43856-023-00370-1 [Google Scholar]
  20. Costello, T. H., Pennycook, G., & Rand, D. G.
    2024 Durably reducing conspiracy beliefs through dialogues with AI. Science, 385(6714), eadq1814. 10.1126/science.adq1814
    https://doi.org/10.1126/science.adq1814 [Google Scholar]
  21. Čyras, K., Rago, A., Albini, E., Baroni, P., & Toni, F.
    2021 Argumentative XAI: a survey. arXiv preprint arXiv:2105.11266.
    [Google Scholar]
  22. 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. 10.1037/amp0001454
    https://doi.org/10.1037/amp0001454 [Google Scholar]
  23. 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]
  24. 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. 10.1016/S2589‑7500(21)00208‑9
    https://doi.org/10.1016/S2589-7500(21)00208-9 [Google Scholar]
  25. Gottlieb, M., & Dyer, S.
    2020 Information and disinformation: social media in the COVID-19 crisis. Academic Emergency Medicine, 27(7), 640–641. 10.1111/acem.14036
    https://doi.org/10.1111/acem.14036 [Google Scholar]
  26. 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. 10.1200/JOP.2012.000800
    https://doi.org/10.1200/JOP.2012.000800 [Google Scholar]
  27. 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. 10.1080/03080188.2020.1840222
    https://doi.org/10.1080/03080188.2020.1840222 [Google Scholar]
  28. 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. 10.2196/jmir.6.3.e36
    https://doi.org/10.2196/jmir.6.3.e36 [Google Scholar]
  29. 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. 10.2196/19702
    https://doi.org/10.2196/19702 [Google Scholar]
  30. Johnson, R. H.
    2000Manifest rationality. A pragmatic theory of argument. Routledge.
    [Google Scholar]
  31. 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. 10.2196/28750
    https://doi.org/10.2196/28750 [Google Scholar]
  32. 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. 10.1016/j.lindif.2023.102274
    https://doi.org/10.1016/j.lindif.2023.102274 [Google Scholar]
  33. 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. 10.1001/jamanetworkopen.2021.13193
    https://doi.org/10.1001/jamanetworkopen.2021.13193 [Google Scholar]
  34. 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. InAAAI Joint Workshop on Health Intelligence (W3PHIAI 2018, pp. 6–13), AAAI.
    [Google Scholar]
  35. 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. 10.1017/XPS.2020.37
    https://doi.org/10.1017/XPS.2020.37 [Google Scholar]
  36. Kurtz, S. M.
    2002 Doctor-patient communication: principles and practices. Canadian Journal of Neurological Sciences, 29(S2), S23–S29. 10.1017/S0317167100001906
    https://doi.org/10.1017/S0317167100001906 [Google Scholar]
  37. 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. 10.1038/s41746‑021‑00384‑w
    https://doi.org/10.1038/s41746-021-00384-w [Google Scholar]
  38. Lawrence, J., & Reed, C.
    2020 Argument mining: a survey. Computational Linguistics, 45(4), 765–818. 10.1162/coli_a_00364
    https://doi.org/10.1162/coli_a_00364 [Google Scholar]
  39. 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. 10.1016/S2589‑7500(22)00003‑6
    https://doi.org/10.1016/S2589-7500(22)00003-6 [Google Scholar]
  40. Longoni, C., Bonezzi, A., & Morewedge, C. K.
    2019 Resistance to medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. 10.1093/jcr/ucz013
    https://doi.org/10.1093/jcr/ucz013 [Google Scholar]
  41. 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. 10.3389/fmed.2024.1504532
    https://doi.org/10.3389/fmed.2024.1504532 [Google Scholar]
  42. Maleki, N., Padmanabhan, B., & Dutta, K.
    2024 AI hallucinations: a misnomer worth clarifying. InProceedings of the 2024 IEEE Conference on Artificial Intelligence (CAI) (pp. 133–138). IEEE. 10.1109/CAI59869.2024.00033
    https://doi.org/10.1109/CAI59869.2024.00033 [Google Scholar]
  43. Mayer, T., Cabrio, E., & Villata, S.
    2020 Transformer-based argument mining for healthcare applications. InG. 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]
  44. 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. 10.1001/jamainternmed.2023.5947
    https://doi.org/10.1001/jamainternmed.2023.5947 [Google Scholar]
  45. 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. 10.1038/s41746‑023‑00873‑0
    https://doi.org/10.1038/s41746-023-00873-0 [Google Scholar]
  46. Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N.
    2022 Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 551, 3503–3568. 10.1007/s10462‑021‑10088‑y
    https://doi.org/10.1007/s10462-021-10088-y [Google Scholar]
  47. 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. 10.1109/JSYST.2018.2890121
    https://doi.org/10.1109/JSYST.2018.2890121 [Google Scholar]
  48. 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. InProceedings of the 12th Argument Mining Workshop (pp. 24–31), ACL. 10.18653/v1/2025.argmining‑1.3
    https://doi.org/10.18653/v1/2025.argmining-1.3 [Google Scholar]
  49. Noy, S., & Zhang, W.
    2023 Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. 10.1126/science.adh2586
    https://doi.org/10.1126/science.adh2586 [Google Scholar]
  50. O’Neil, C.
    2016Weapons of math destruction. Crown Books.
    [Google Scholar]
  51. Paglieri, F.
    2024 Expropriated minds: on some practical problems of generative AI, beyond our cognitive illusions. Philosophy & Technology, 37(2), 1–30. 10.1007/s13347‑024‑00743‑x
    https://doi.org/10.1007/s13347-024-00743-x [Google Scholar]
  52. Piantadosi, S. T., & Hill, F.
    2022 Meaning without reference in large language models. arXiv preprint arXiv:2208.02957.
    [Google Scholar]
  53. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J.
    2022 AI in health and medicine. Nature Medicine, 28(1), 31–38. 10.1038/s41591‑021‑01614‑0
    https://doi.org/10.1038/s41591-021-01614-0 [Google Scholar]
  54. Ray, P. P.
    2024 Can LLMs improve existing scenario of healthcare?. Journal of Hepatology, 80(1), e28–e29. 10.1016/j.jhep.2023.08.006
    https://doi.org/10.1016/j.jhep.2023.08.006 [Google Scholar]
  55. 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]
  56. 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. 10.2196/jmir.3588
    https://doi.org/10.2196/jmir.3588 [Google Scholar]
  57. 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. 10.1016/j.im.2022.103595
    https://doi.org/10.1016/j.im.2022.103595 [Google Scholar]
  58. 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. 10.1186/1471‑2296‑8‑47
    https://doi.org/10.1186/1471-2296-8-47 [Google Scholar]
  59. Swire-Thompson, B., & Lazer, D.
    2020 Public health and online misinformation: challenges and recommendations. Annual Review of Public Health, 41(1), 433–451. 10.1146/annurev‑publhealth‑040119‑094127
    https://doi.org/10.1146/annurev-publhealth-040119-094127 [Google Scholar]
  60. 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. 10.2196/jmir.5729
    https://doi.org/10.2196/jmir.5729 [Google Scholar]
  61. 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. 10.1038/s41467‑024‑45879‑8
    https://doi.org/10.1038/s41467-024-45879-8 [Google Scholar]
  62. 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. 10.1038/s41591‑023‑02448‑8
    https://doi.org/10.1038/s41591-023-02448-8 [Google Scholar]
  63. Topol, E. J.
    2019 High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. 10.1038/s41591‑018‑0300‑7
    https://doi.org/10.1038/s41591-018-0300-7 [Google Scholar]
  64. Vassiliades, A., Bassiliades, N., & Patkos, T.
    2021 Argumentation and explainable artificial intelligence: a survey. The Knowledge Engineering Review, 361, e5. 10.1017/S0269888921000011
    https://doi.org/10.1017/S0269888921000011 [Google Scholar]
  65. Walters, W. H., & Wilder, E. I.
    2023 Fabrication and errors in the bibliographic citations generated by ChatGPT. Scientific Reports, 13(1), 14045. 10.1038/s41598‑023‑41032‑5
    https://doi.org/10.1038/s41598-023-41032-5 [Google Scholar]
  66. Wang, D., & Zhang, S.
    2024 Large language models in medical and healthcare fields: applications, advances, and challenges. Artificial Intelligence Review, 57(11), 299. 10.1007/s10462‑024‑10921‑0
    https://doi.org/10.1007/s10462-024-10921-0 [Google Scholar]
  67. 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. 10.2196/22769
    https://doi.org/10.2196/22769 [Google Scholar]
  68. Yang, Y., & Ngai, E. W.
    2024 The influence of physician stance on patient resistance to healthcare AI. InProceedings of AMCIS 2024 (vol.41, pp. 2656–2660). Association for Information Systems (AIS).
    [Google Scholar]
  69. 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. 10.1016/j.im.2024.103961
    https://doi.org/10.1016/j.im.2024.103961 [Google Scholar]
  70. 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. 10.2196/66207
    https://doi.org/10.2196/66207 [Google Scholar]
/content/journals/10.1075/jaic.25024.pag
Loading
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error