Physician and artificial intelligence chatbot responses to cancer questions from social media
Open accessAbstract
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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Empirical evaluations have considered response quality, empathy, and unsafe answers.
“while empirical evaluations have considered response quality, empathy, and unsafe answers”
From Mapping Technologies, Support Functions, Evaluation Outcomes, and Safety Governance Related to Conversational AI Agents for Psychological Support of Patients With Cancer: Scoping Review (2026) · cites this paper as [15]
The Methods and Results report physician ratings of response quality and empathy, including medical accuracy. They do not report an evaluation of unsafe answers as a separate outcome; the paper’s recommendation for clinician oversight does not establish that such answers were measured. Page numbers were not provided in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAI-generated responses, particularly those from Claude AI, outperformed those from oncologists in terms of empathy, clarity, and emotional resonance.
“The study found that AI-generated responses, particularly those from Claude AI, outperformed those from oncologists in terms of empathy, clarity, and emotional resonance”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [19]
The findings of mixed performance align with similar reports of mixed performance across different tasks and clinical text datasets within cancer type-specific domains.
“These findings align with similar reports of mixed performance across different tasks and clinical text datasets within cancer type-specific domains [45-47]”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [45]
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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
- ORCID
- Venue
- JAMA Oncology
- Publisher
- American Medical Association (AMA)
- Published
- 2024-07-01
- Volume
- 10
- Issue
- 7
- Pages
- 956
- ISSN
- 2374-2437 (ISSN-L)
- PMC
- PMC11099835
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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