With the rapid penetration of artificial intelligence (AI) in healthcare, its associated ethical issues have become increasingly prominent. However, existing research often lacks systematic approaches and fails to explore cognitive differences thoroughly among healthcare professionals across regions, professions, and departments. To address this gap, this study systematically retrieved 19 qualitative studies from Embase, PubMed, and Web of Science databases. Quality was assessed using the JBI-QARI tool, and data were analyzed through thematic analysis, encompassing healthcare professionals from diverse backgrounds. Findings reveal that while AI enhances diagnostic accuracy and optimizes resource allocation, it also triggers ethical dilemmas such as algorithmic bias, data privacy breaches, and ambiguous accountability. Furthermore, cultural, resource, and policy disparities across regions significantly influence healthcare professionals’ perceptions, while differing professional roles and departmental responsibilities lead to distinct ethical priorities. Thus, AI applications in healthcare face multidimensional ethical challenges that disrupt practitioners’ workflows while profoundly impacting patient rights protection and healthcare system operations. Future efforts must develop systematic solutions across technological R&amp;D, responsibility allocation, data security, and personnel training to balance innovation with ethics and advance sustainable AI-driven healthcare. 

Ethical concerns of AI in healthcare: A systematic review of qualitative studies

Artificial intelligence (AI) systems offer immense potential at improving care processes, such as diagnostic support and automating clinical documentation. However, before these systems’ benefits can be realized, these systems first must be trusted by healthcare professionals. With an increasing number of studies that report on how trust in AI is formed, there is a need to summarize this literature. To address this gap, we aimed to conduct a review of existing empirical literature to identify factors associated with purpose-, process-, and performance-related trust of AI tools among healthcare professionals. Based on 75 studies, we found that most studies examined process- and performance-related trust. Overall, some factors influenced both process- and performance-related trust, such as endorsement by individuals within the health care organization and explainable AI. Other factors seemed to influence a specific level of trust (e.g., higher accuracy from the AI model and performance-related trust). Broadly, further studies are still needed to study purpose-related trust, develop validated measurement tools, examine differences between types of healthcare professionals, and understand how trust changes longitudinally. 

Factors Associated with Purpose-, Process-, and Performance-related Trust for Artificial Intelligence Technologies for Clinical Practice Among Healthcare Professionals: A Review of Empirical Research

Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.

Artificial Intelligence in Medical Care – Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study

Artificial intelligence (AI) is rapidly reshaping the medical field. This study aimed to investigate the attitudes of physicians towards AI in medical care using focus groups. Most participants seemed to be open to the use of AI in medicine if transparency in AI systems is ensured, regulatory barriers are addressed and personal contact to the patient is maintained. This qualitative study allows insights into how German physicians perceive the use of AI in medical care. Gaining input from physicians is important when designing future applications of AI for the practical use in medical care.

Physicians' Attitudes Towards Artificial Intelligence: Results of the PEAK Project.

BACKGROUND
Artificial intelligence (AI) in medicine is a very topical issue. As far as the attitudes and perspectives of the different stakeholders in healthcare are concerned, there is still much to be explored.


OBJECTIVE
Our aim was to determine attitudes and aspects towards acceptance of AI applications from the perspective of physicians in university hospitals.


METHODS
We conducted individual exploratory expert interviews. Low fidelity mockups were used to show interviewees potential application areas of AI in clinical care.


RESULTS
In principle, physicians are open to the use of AI in medical care. However, they are critical of some aspects such as data protection or the lack of explainability of the systems.


CONCLUSION
Although some trends in attitudes e.g., on the challenges or benefits of using AI became clear, it is necessary to conduct further research as intended by the subsequent PEAK project.

Attitudes and Acceptance Towards Artificial Intelligence in Medical Care

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Papers

Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.

Artificial Intelligence in Medical Care – Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study

Physicians' Attitudes Towards Artificial Intelligence: Results of the PEAK Project.

Attitudes and Acceptance Towards Artificial Intelligence in Medical Care