The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector
Open accessAbstract
The healthcare sector is faced with challenges due to a shrinking healthcare workforce and a rise in chronic diseases that are worsening with demographic and epidemiological shifts. Digital health interventions that include artificial intelligence (AI) are being identified as some of the potential solutions to these challenges. The ultimate aim of these AI systems is to improve the patient's health outcomes and satisfaction, the overall population's health, and the well-being of healthcare professionals. The applications of AI in healthcare services are vast and are expected to assist, automate, and augment several healthcare services. Like any other emerging innovation, AI in healthcare also comes with its own risks and requires regulatory controls. A review of the literature was undertaken to study the existing regulatory landscape for AI in the healthcare services sector in developed nations. In the global regulatory landscape, most of the regulations for AI revolve around Software as a Medical Device (SaMD) and are regulated under digital health products. However, it is necessary to note that the current regulations may not suffice as AI-based technologies are capable of working autonomously, adapting their algorithms, and improving their performance over time based on the new real-world data that they have encountered. Hence, a global regulatory convergence for AI in healthcare, similar to the voluntary AI code of conduct that is being developed by the US-EU Trade and Technology Council, would be beneficial to all nations, be it developing or developed.
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“Measurement and aggregation bias (n = 6) was one of the most frequently identified biases in this phase, as many AI models adopt a “one-size-fits-all” approach, assuming population averages are sufficient to address subgroup-specific variations [52,53].”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [52]
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Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use
Pham, T. D. (2025) · Royal Society Open Science · doi:10.1098/rsos.241873 · Cited by 367
Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes
Mazumdar, H., Khondakar, K. R., Das, S., Halder, A., & Kaushik, A. K. (2024) · Expert Opinion on Drug Delivery · doi:10.1080/17425247.2024.2440618 · Cited by 180
Explainable Artificial Intelligence (XAI)
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Metadata
- Authors
- Palaniappan, K., Lin, E. Y. T., & Vogel, S.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Palaniappan, Kavitha
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2024-02-28
- Volume
- 12
- Issue
- 5
- Pages
- 562
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC10930608
- OpenAlex
- W4392293571
- Cited by
- 301
- References
- 33
- Citations by year
- 2026: 107 · 2025: 168 · 2024: 26
- FWCI
- 91.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Ministry of Health (MH 114:63/5)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Regulation
- Keywords
- artificial intelligence in healthcare, AI regulation, regulatory framework, software as a medical device, digital health products, regulatory convergence, autonomous AI systems, health workforce shortage, chronic disease
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