ArticlePloS one2025
Societal factors influencing the implementation of AI-driven technologies in (smart) hospitals.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The AI-Driven Healthcare Value Framework-Rethinking Traditional Care Models in the Age of Automation.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Social Health: A Narrative Review of Uses, Advantages, Challenges, and Future Directions.Healthcare (Basel, Switzerland) · 2026Review
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Anemia in young women: determinants and artificial intelligence-based management approaches.Frontiers in artificial intelligence · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe introduction of AI in healthcare promises benefits, but also faces challenges. Currently, one of these challenges is the lack of information on the societal aspects of implementing AI in healthcare. This study aims to: 1) identify which societal factors play a key role in the implementation of AI-driven technology in (smart) hospitals according to different stakeholder groups; 2) examine how these factors play a role within (smart) hospitals by discussing their facilitators, barriers, possibilities, and preconditions; and 3) develop a societal guide to serve as a roadmap for an implementation process of AI in a healthcare setting.
methodsA survey was conducted, followed by four focus group interviews (FGIs). In the survey, participants (n = 7) assessed the relevance of factors for inclusion in the FGIs using a rating scale from 1 to 5 (1 = irrelevant, 5 = relevant). In each FGI, 2-3 participants discussed how these societal factors play a role in the implementation of AI technology in (smart) hospitals. By combining and categorizing these insights, a societal guide was set up to provide a structured approach for implementation of AI-driven healthcare innovation.
resultsThe survey revealed that 9 out of 10 proposed factors were considered relevant (90%). The FGIs demonstrated uncertainty surrounding the (future) use of AI technologies within (smart) hospitals. As this field is still in its early stages, there are limited established methodologies and (regulatory and ethical) frameworks for implementation. While much knowledge exists on different factors concerning AI in (smart) hospitals, this knowledge is often siloed. This knowledge must be integrated across stakeholders to adequately prepare for the deployment of AI technologies. The societal guide developed addresses ethical and regulatory considerations, while also covering important human-centred factors for AI implementation in healthcare.
conclusionEngaging various stakeholders throughout different phases of AI implementation in (smart) hospitals (i.e., development, implementation, monitoring and evaluation phase) is key for fostering a collaborative approach. Recognizing the interdependence and collective impact of factors is essential for creating a successful implementation trajectory.
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