JMIR Publications

ABSTRACT

Background:

The application of artificial intelligence in clinical settings is becoming increasingly widespread, exerting a profound impact on healthcare professionals’ work patterns, decision making efficiency, and clinical practice. However, there is currently a lack of systematic research on healthcare professionals’ actual experiences, barriers to use, and attitudes toward the clinical application of AI. AI has shown great potential to streamline clinical workflows and improve service quality, yet its real world adoption faces notable obstacles.

Objective:

This study aimed to comprehensively explore the multifaceted challenges encountered during AI implementation in routine healthcare work. The findings intend to offer empirical evidence and practical references for improving the deployment, management and popularization of AI tools in clinical settings.

Methods:

A qualitative design with inductive thematic analysis was adopted. Through purposive sampling, 23 healthcare workers with diverse job roles, professional titles and working experience were recruited for one-on-one semi-structured interviews. Two researchers independently performed data coding and cross-checked results to guarantee inter-coder reliability. NVivo 15.0 software was utilized to organize qualitative data and support thematic analysis.

Results:

All data were fully transcribed, the interviews identified 5 themes and 13 sub-themes related to the challenges of applying AI in the healthcare sector. The 5 themes are as follows: Construction of AI Trust Mechanisms and Shaping of Rational Cognition; AI-Assisted Decision-Making Models and Healthcare Collaboration; AI User Experience; Barriers to AI Use and Risk Concerns; and Attitudes Towards AI Substitution and Perception of Core Healthcare Value. Participants generally recognized the value of AI in clinical work, yet highlighted multiple practical barriers and potential risks.

Conclusions:

The application of artificial intelligence in healthcare is shaped by the interplay of complex factors spanning technology, organization, cognition and regulation. A balanced perspective is essential to ensure that AI integration improves rather than compromises clinical practice. While AI holds great potential for boosting efficiency and supporting clinical decision-making, it also alerts us to risks including over-reliance, cognitive decline, and the long-term erosion of professional skills and independent clinical reasoning.Accordingly, efforts should be made to optimize the design and deployment of AI tools, alongside a fundamental rethink of professional training and governance frameworks. Future work should prioritize developing intelligence-augmenting rather than intelligence-replacing systems. Critical evaluation of AI outputs needs to be embedded into clinical education, and relevant policies should be formulated to guard against cognitive complacency. The ultimate goal is to achieve sound, sustainable and people-centered integration of AI into routine clinical practice. Clinical Trial: Ethical approval was obtained from Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University 2025-KYC(0531).