ArticleBMJ open2025
Exploring the social dimensions of AI integration in healthcare: a qualitative study of stakeholder views on challenges and opportunities.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI Perceptions, Professional Identity, and AI-Supported Clinical Decisions Among Medical Students and Clinicians: Cross-Sectional Survey and Quasi-Randomized Vignette Study.JMIR medical education · 2026Article
- Perceived Algorithmic Care Intensity and Older Adults' Autonomy in Community Smart Care: Mixed Methods Study.JMIR aging · 2026Article
- Artificial Intelligence in Social Health: A Narrative Review of Uses, Advantages, Challenges, and Future Directions.Healthcare (Basel, Switzerland) · 2026Review
- "Black box" artificial intelligence for mortality prediction: a mixed-methods study of palliative care team, patient, and caregiver perspectives.Annals of palliative medicine · 2026Article
- AI in medical and dentistry education: perspectives from international students, educators and physicians.BMC medical education · 2026Article
- When Intuition Meets the Algorithm: Medico-Legal Implications of Artificial Intelligence-Driven Decision-Making in Orthopedics.Bioengineering (Basel, Switzerland) · 2026Review
- How hospital accreditation requirements bridge enablers for AI readiness: interpretative analysis of intersections in framework standards.Frontiers in digital health · 2026Article
- Artificial intelligence in dementia care: challenges, controversies, and policy implications.Frontiers in dementia · 2026Article
- Factors Influencing Adoption of Large Language Models in Health Care: Multicenter Cross-Sectional Mixed Methods Observational Study.Journal of medical Internet research · 2025Observational
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Authors and funding
8 authors.
Funding
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Abstract
objectivesThis study aimed to investigate the opportunities and challenges associated with integrating artificial intelligence (AI) in healthcare by exploring the perspectives of various stakeholders. The objective was to provide a nuanced understanding of stakeholder views to address concerns and promote the acceptance and successful integration of AI technologies in medical practice.
designThis exploratory qualitative study used semi-structured interviews. Data were analysed using a combination of deductive and inductive coding, followed by content analysis to identify and develop categories.
settingThis study was conducted in Tübingen, Germany, within the framework of the TüKITZMed project (Tübingen AI Training Center for Medicine), between August 2022 and March 2023.
participantsA total of 38 stakeholders participated, including 6 lecturers, 9 clinicians, 10 healthcare students, 6 AI experts and 7 institutional stakeholders. Inclusion criteria included professionals involved in or affected by AI in healthcare, while exclusion criteria comprised individuals without relevant experience.
interventionsNot applicable. OUTCOME MEASURES: The main outcome was the identification of thematic categories capturing stakeholders' perceptions, expectations and concerns regarding the integration of AI in healthcare.
resultsThe analysis identified two main thematic categories: two main categories encompassing a total of 14 subcategories: (1) perceived opportunities of AI in medicine, including aspects of increased efficiency, reduced workload and improved patient safety and (2) perceived challenges of AI in medicine, such as its impact on medical decision-making and concerns about dependence on technology. These themes reflect diverse perspectives and insights across stakeholder groups.
conclusionsDiverse stakeholder perspectives offer valuable insights into the anticipated effects of AI in healthcare. Understanding these perspectives can support decision-makers in designing context-sensitive AI strategies and identifying areas for further professional and institutional development. Future research should monitor how these attitudes evolve in response to technological progress and real-world implementation.
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