Success Factors of Artificial Intelligence Implementation in Healthcare
Open accessAbstract
Background: Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare. Methods: A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database. The according search queries have been defined based on success factor categories for AI implementation derived from a prior World Health Organization survey about barriers of adoption of Big Data within 125 countries. The eligible publications and real life cases were identified through a catalog of in- and exclusion criteria focused on concrete AI application cases. These were then analyzed to deduct and discuss success factors that facilitate or inhibit a broad-scale implementation of AI in healthcare. Results: The analysis revealed three categories of success factors, namely (1) policy setting, (2) technological implementation, and (3) medical and economic impact measurement. For each of them a set of recommendations has been deducted: First, a risk adjusted policy frame is required that distinguishes between precautionary and permissionless principles, and differentiates among accountability, liability, and culpability. Second, a “privacy by design” centered technology infrastructure shall be applied that enables practical and legally compliant data access. Third, the medical and economic impact need to be quantified, e.g., through the measurement of quality-adjusted life years while applying the CHEERS and PRISMA reporting criteria. Conclusions: Private and public institutions can already today leverage AI implementation based on the identified results and thus drive the translation from scientific development to real world application. Additional success factors could include trust-building measures, data categorization guidelines, and risk level assessments and as the success factors are interlinked, future research should elaborate on their optimal interaction to utilize the full potential of AI in real world application.
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Lack of clinical and economic measurements is a barrier to practical implementation.
“Wolff et al [78] particularly noted the lack of clinical and economic measurements as a barrier to practical implementation.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [78]
Lack of regulation and policies from the government, including uncertainty around legal direction or law, is a barrier to the application of AI technologies.
“A lack of regulation and policies from the government, including uncertainty around legal direction or law, was presented as a barrier to the application of AI technologies [37,45,57,60,77,78].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [78]
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Beyond Automation: Unleashing Creativity and Enhancing Decision-Making with Generative AI
Ishaq, R., Gorsi, H., Butt, S., Umair, T., & Tajammal, R. (2025) · ACADEMIA International Journal for Social Sciences · doi:10.63056/acad.004.01.0142 · Cited by 1 · see citing works
Leveraging AI to Enhance Healthcare Delivery in Tanzania: Innovations and Ethical Imperatives
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Role of Artificial Intelligence in Medical Predictions, Interventions and Quality of Life
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- Authors
- Wolff, J., Pauling, J., Keck, A., & Baumbach, J.
- Affiliations
- Technical University of Munich (DE)
- Universität Hamburg (DE)
- Corresponding author
- Wolff, Justus
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2021-06-16
- Volume
- 3
- Pages
- 594971-594971
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC8521923
- OpenAlex
- W3169649689
- Cited by
- 104
- References
- 30
- Citations by year
- 2026: 15 · 2025: 38 · 2024: 20 · 2023: 16 · 2022: 12 · 2021: 3
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- mediaTUM (Technical University of Munich) (submittedVersion, cc-by)
- Funders
- Horizon 2020 Framework Programme (777111, 826078)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, technology adoption barriers, health policy, privacy by design, data protection, economic impact measurement, quality-adjusted life years
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