ChatGPT vs. surgeons on pancreatic cancer queries: accuracy & empathy evaluated by patients and experts
Open accessAbstract
BACKGROUND: Artificial intelligence (AI) offers potential support in patient-clinician interactions, but its impact on such communication remains unexplored. METHODS: In this study, ChatGPT was compared with two pancreatic surgeons in responding to ten pancreatic cancer surgery-related questions, co-designed with the Patient Advisory Board of the Surgical Society's Study Center. A blind evaluation of these responses, considering content congruency and clarity for non-specialists, was conducted by patients and surgeons. RESULTS: From June 23 to July 21, 2023, 24 patients and 25 surgeons participated, of which eleven patients and ten surgeons completed the survey in full. Utilizing a quantitative scale from 1 (strong-disagreement) to 5 (full-agreement), consensus was observed among patients and specialists concerning the content delivered by ChatGPT. The metrics for comprehensibility to a non-specialist audience consistently showed positive reception. In the evaluation of empathetic resonance, ChatGPT's responses mirrored those of the surgeons in the patient's view. A significant proportion ranked Surgeon 1's contributions foremost, followed closely by ChatGPT. DISCUSSION: This study demonstrates that surgeons and ChatGPT answer common queries from patients regarding pancreatic cancer surgery comparable regarding reliability, lay comprehension and empathy as evaluated by patients and surgical experts. These findings highlight the potential of AI in enhancing patient-provider interactions.
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AI personalized conversations and treatment recommendations help patients make informed decisions and improve treatment adherence.
“This helps patients make informed decisions and improves treatment adherence [57].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [57]
The Discussion reports that ChatGPT’s answers to general pancreatic-cancer surgery questions were understandable and showed promise for patient education. It does not establish that personalized conversations or treatment recommendations helped patients make informed decisions, and it did not assess treatment adherence. (LLM, conf=0.80)
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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
O'Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., Holzinger, K., Holzinger, A., Sajid, M. I., & Ashrafian, H. (2019) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1968 · Cited by 437 · see citing works
Hopkins, A. M., Logan, J. M., Kichenadasse, G., & Sorich, M. J. (2023) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkad010 · Cited by 259 · see citing works
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Research works printed but not linked: 2
- Antel R, Abbasgholizadeh-Rahimi S, Guadagno E, Harley JM, Poenaru D. (2022) The use of artificial intelligence and virtual reality in doctor-patient risk communication: a scoping review. Patient Educ Counsel 105:3038-3050.
- Flesch R. (1948) A new readability yardstick. J Appl Psychol 32: 221-233. https://doi.org/10.1037/h0057532. · DOI · Paper page
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White, B. M., Aitken, G. L., Zink, J. A., Kheirkhah Rahimabad, P., Kumsa, F. A., Hashtarkhani, S., Rashid, R., Kheirinejad, S., Girdwood, T., Brett, C. L., et al. (2026) · JMIR Cancer · doi:10.2196/82336 · Cited by 2 · see citing works
“Study characteristics are summarized in Table S1 in Multimedia Appendix 1 [ 14-57 14-57 ].”
- Authors
- Klotz, R., Pausch, T. M., Kaiser, J., Joos, M. C., Hecktor, R., Ahmed, A., Dörr-Harim, C., Mehrabi, A., Loos, M., Roth, S., Michalski, C. W., & Kahlert, C.
- Affiliations
- University Hospital Heidelberg (DE)
- National Center for Tumor Diseases (DE)
- Corresponding author
- Kahlert, Christoph
- ORCID
- Venue
- HPB
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 27
- Issue
- 3
- Pages
- 311-317
- ISSN
- 1365-182X (ISSN-L)
- OpenAlex
- W4404976008
- Cited by
- 18
- References
- 3
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 1
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- HPB (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- ChatGPT, artificial intelligence, pancreatic cancer surgery, surgeon-patient communication, patient-provider interaction, empathy, patient understanding, patient advisory board
- MeSH terms
- Generative Artificial Intelligence, Adult, Aged, Artificial Intelligence, Attitude of Health Personnel, Communication, Empathy, Female, Humans, Male, Middle Aged, Pancreatic Neoplasms, Physician-Patient Relations, Surveys and Questionnaires, Comprehension, Social Media, Surgeons
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