Cited by 120Artificial Intelligence in Healthcare and EducationHealth Literacy and Information AccessibilitySocial Media in Health Education

Physician and artificial intelligence chatbot responses to cancer questions from social media

Open access
Chen, David, Parsa, Rod, Hope, Andrew, Hannon, Breffni, Mak, Ernie, Eng, Lawson, Liu, Fei-Fei, Fallah-Rad, Nazanin +2 more·2024·JAMA Oncology

Abstract

Importance: Artificial intelligence (AI) chatbots pose the opportunity to draft template responses to patient questions. However, the ability of chatbots to generate responses based on domain-specific knowledge of cancer remains to be tested. Objective: To evaluate the competency of AI chatbots (GPT-3.5 [chatbot 1], GPT-4 [chatbot 2], and Claude AI [chatbot 3]) to generate high-quality, empathetic, and readable responses to patient questions about cancer. Design, Setting, and Participants: This equivalence study compared the AI chatbot responses and responses by 6 verified oncologists to 200 patient questions about cancer from a public online forum. Data were collected on May 31, 2023. Exposures: Random sample of 200 patient questions related to cancer from a public online forum (Reddit r/AskDocs) spanning from January 1, 2018, to May 31, 2023, was posed to 3 AI chatbots. Main Outcomes and Measures: The primary outcomes were pilot ratings of the quality, empathy, and readability on a Likert scale from 1 (very poor) to 5 (very good). Two teams of attending oncology specialists evaluated each response based on pilot measures of quality, empathy, and readability in triplicate. The secondary outcome was readability assessed using Flesch-Kincaid Grade Level. Results: Responses to 200 questions generated by chatbot 3, the best-performing AI chatbot, were rated consistently higher in overall measures of quality (mean, 3.56 [95% CI, 3.48-3.63] vs 3.00 [95% CI, 2.91-3.09]; P < .001), empathy (mean, 3.62 [95% CI, 3.53-3.70] vs 2.43 [95% CI, 2.32-2.53]; P < .001), and readability (mean, 3.79 [95% CI, 3.72-3.87] vs 3.07 [95% CI, 3.00-3.15]; P < .001) compared with physician responses. The mean Flesch-Kincaid Grade Level of physician responses (mean, 10.11 [95% CI, 9.21-11.03]) was not significantly different from chatbot 3 responses (mean, 10.31 [95% CI, 9.89-10.72]; P > .99) but was lower than those from chatbot 1 (mean, 12.33 [95% CI, 11.84-12.83]; P < .001) and chatbot 2 (mean, 11.32 [95% CI, 11.05-11.79]; P = .01). Conclusions and Relevance: The findings of this study suggest that chatbots can generate quality, empathetic, and readable responses to patient questions comparable to physician responses sourced from an online forum. Further research is required to assess the scope, process integration, and patient and physician outcomes of chatbot-facilitated interactions.

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Crossref · 10.1001/jamaoncol.2024.0836

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References 9 · Cited by 120

Authors
Chen, D., Parsa, R., Hope, A., Hannon, B., Mak, E., Eng, L., Liu, F.-F., Fallah-Rad, N., Heesters, A. M., & Raman, S.
Affiliations
  • University of Toronto (CA)
  • Princess Margaret Cancer Centre (CA)
  • University Health Network (CA)
  • Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre/University Health Network Toronto, Toronto, Ontario, Canada
Corresponding author
Raman, Srinivas
Venue
JAMA Oncology
Published
2024-07-01
Volume
10
Issue
7
Pages
956
ISSN
2374-2437 (ISSN-L)
OpenAlex
W4396977150
Cited by
120
References
9
Citations by year
2026: 49 · 2025: 53 · 2024: 18
FWCI
11.22
Type
journal article
Language
en
Open access
Locations
  • JAMA Oncology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
  • Social Sciences › Health › Social Media in Health Education
Keywords
AI chatbots, physician responses, readability, response quality, GPT-4, Claude AI, online patient forums, oncology communication, patient-provider communication, AI-generated health information, Flesch-Kincaid Grade Level
MeSH terms
Artificial Intelligence, Empathy, Female, Humans, Male, Neoplasms, Physician-Patient Relations, Physicians, Social Media

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