ReviewJournal of clinical medicine2026
Guiding Policymakers Toward Better AI Ethics Integration in Healthcare Regulation-Lessons from Singapore.
Review in Journal of clinical medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In terms of rollout, comprehensiveness, and strategy, Singapore's regulatory landscape governing the ethical use of Artificial Intelligence (AI) in healthcare has generally kept pace with other global leaders in AI advancement. However, establishing a robust and holistic regulatory framework that evolves along with emerging technologies is not easy-especially in healthcare, where the stakes are high and resources may be limited. We conducted a structured scoping analysis of key AI regulatory and professional documents in Singapore, selected using predefined inclusion criteria. Documents were systematically mapped against Savulescu et al.'s nine categories of ethical risk, followed by cross-document comparison to identify integration gaps and inconsistencies, and benchmarking against international AI governance frameworks. These recommendations are generalizable beyond Singapore for developers, implementers, healthcare professionals and patients and include dealing with bias in AI, enhancing human productivity without deskilling, facilitating more informed decision-making, and cultivating greater knowledge exchange between clinicians and patients, to name a few.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.