JMIR Publications

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. However, limited literature has examined the perceptions and attitudes of oncologists in terms of endorsing AI-driven chatbots for health information.

Objective:

To explore the perceptions and attitudes of Chinese oncologists toward endorsing AI-driven chatbots to cancer patients.

Methods:

In this phenomenological qualitative study, we purposively sampled oncologists from four hospitals in Southwest and East China and conducted semi-structured interviews with 24 participants between November 19, 2024, and December 21, 2024. Data saturation principle was observed to determine end point of data collection. Data were analyzed using Colaizzi’s method.

Results:

The participants were aged 42.0 years on average (range 29-53 years), including 9 females (37.5%) and 15 males (62.5%). The participants had an average of 8.8 years in oncology (range 1-25 years). Of the participants, 7 (29.2%) had recommended AI chatbots to patients. Three key themes were revealed from analysis of interview transcriptions, including perceived benefits, significant concerns, and impacts on doctor-patient dynamics. Benefits included enhanced accessibility and potential support for chronic condition management. Concerns centered on liability, misinformation, lack of personalization, privacy and data security risks, and patient readiness and education. Oncologists stressed a dual impact of AI chatbots on doctor-patient dynamics, recognizing potential for improved communication and risks of trust erosion due to over-reliance on AI.

Conclusions:

While recognizing the potential of AI-driven chatbots to enhance accessibility of health information and chronic disease management, Chinese oncologists report significant concerns, including liability, misinformation, lack of personalization, privacy and data security risks, and patient readiness. Addressing the challenges requires comprehensive solutions, such as clear policies and guidelines, rigorous testing and validation, institutional endorsement, and robust patient and provider education. Future efforts should focus on resolving the barriers while leveraging the strengths of AI technology to support patient-centered care in a safe, effective, and ethical manner.