Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 17, 2025
Date Accepted: Jun 12, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Perceptions and attitudes of Chinese oncologists towards recommending AI-driven chatbots for cancer patient health information seeking: A phenomenological qualitative study
ABSTRACT
Background:
Chatbots driven by Large language model (LLM) artificial intelligence (AI) have emerged as potential tools to enhance health information access for cancer patients. However, their integration into patient education raises concerns among oncologists.
Objective:
This study aims to explore the perceptions and attitudes of Chinese oncologists toward recommending AI-driven chatbots to cancer patients.
Methods:
This phenomenological qualitative study included semi-structured interviews with 24 oncologists from four hospitals in Southwest and East China. Participants were purposively sampled based on their experience in oncology and exposure to AI-driven chatbots. Data were analyzed using Colaizzi’s method.
Results:
Three themes emerged: perceived benefits, significant concerns, and impacts on doctor-patient dynamics. Benefits included enhanced accessibility and support for chronic disease management. Concerns centered on liability, misinformation, lack of personalization, and privacy risks. Oncologists highlighted the dual impact of chatbots on doctor-patient dynamics, recognizing potential for improved communication but also risks of trust erosion due to over-reliance on AI.
Conclusions:
While oncologists recognize the potential of AI-driven chatbots in oncology, concerns about their reliability, liability, and patient readiness limit their widespread adoption. Comprehensive strategies, including policy guidelines, rigorous validation, and provider-patient education, are essential to address these barriers and ensure the safe integration of AI in oncology care.
Citation
