Evidence map›Paper›PMID 40699917›Full record

ArticleJournal of medical Internet research2025

Perceptions and Attitudes of Chinese Oncologists Toward Endorsing AI-Driven Chatbots for Health Information Seeking Among Patients with Cancer: Phenomenological Qualitative Study.

Lijuan Zeng, Qiaoqi Li, Yan Zuo, Ying Zhang, Zhaojun Li

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lijuan ZengDivision of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0009-0008-7515-3606
Qiaoqi LiDivision of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0002-1091-2957
Yan ZuoDepartment of Gynecology and Obstetrics Nursing, West China Second University Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0003-1397-013X
Ying ZhangDivision of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0001-7250-5962
Zhaojun LiDepartment of Radiation Oncology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), #19 Xiuhua Road, Xiuying District, Haikou, 570311, China, 86 18898963083.ORCID http://orcid.org/0009-0005-5702-9837

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chatbots driven by large language model artificial intelligence (AI) have emerged as potential tools to enhance health information access for patients with cancer. However, their integration into patient education raises concerns among oncologists. Limited literature has examined the perceptions and attitudes of oncologists in terms of endorsing AI-driven chatbots for health information. Objective: This study aims to explore the perceptions and attitudes of Chinese oncologists toward endorsing AI-driven chatbots to patients with cancer. Methods: In this phenomenological qualitative study, we purposively sampled oncologists from 4 hospitals in Southwest and East China and conducted semistructured interviews with 24 participants between November 19, 2024, and December 21, 2024. The data saturation principle was observed to determine the end point of data collection. Data were analyzed using the Colaizzi method. Results: The participants were aged 42.0 (range 29-53) years on average, including 9 (37%) female and 15 (62%) male participants. The participants had an average of 8.8 (range 1-25) years in oncology. Of the participants, 7 (29%) 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 the potential for improved communication and risks of trust erosion due to overreliance 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.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelInformation Seeking BehaviorNeoplasmsOncologistsAdultChinaEast Asian PeopleFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPhysician-Patient RelationsQualitative Researchartificial intelligenceattitudechatbothealth information seekinglarge language modelliabilitymisinformationoncologistpatient educationperceptionqualitative study

Identifiers

PMID40699917
PMCPMC12309621

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.