Evidence map›Paper›PMID 38470673›Full record

ReviewHealthcare (Basel, Switzerland)2024

Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector.

Kavitha Palaniappan, Elaine Yan Ting Lin, Silke Vogel

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

103 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Article
  8. Review
  9. Article
  10. AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026
    Review
  11. Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026
    Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Artificial intelligence in orthopaedics: Enhanced examinations, ambient intelligence and the future of clinical practice.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
  17. Review
  18. Article
  19. Review
  20. Review

43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kavitha PalaniappanCentre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore.
Elaine Yan Ting LinCentre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore.
Silke VogelCentre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore.

Funding

Ministry of Health MH 114:63/5
6 · The paper itself

Abstract

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.

Indexed as

artificial intelligencehealthcare servicesregulatory frameworks

Identifiers

PMID38470673
PMCPMC10930608

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.