SynthesisFrontiers in digital health2025
The evolving literature on the ethics of artificial intelligence for healthcare: a PRISMA scoping review.
Synthesis in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Artificial Intelligence (AI) Implementation in Maternal and Child Health: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Social health insurance and professional ethics in Romania: between legal framework and medical integrity.Frontiers in health services · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
This scoping review analyzes the literature on the ethics of artificial intelligence (AI) tools in healthcare to identify trends across populations and shifts in published research between 2020 and 2024. We conducted a PRISMA scoping review using structured searches in PubMed and Web of Science for articles published from 2020 to 2024. After removing duplicates, the study team screened all sources at three levels for eligibility (title, abstract, and full text). We extracted data from sources using a Qualtrics questionnaire. We conducted data cleaning and descriptive statistical analyses using R version 4.3.1. A total of 309 sources were included in the analysis. While most sources were conceptual articles, the number of empirical studies increased over time. Commonly addressed ethical concerns included bias, transparency, justice, accountability, privacy/confidentiality, and autonomy. In contrast, disclosure of AI-generated results to patients was infrequently addressed. There was no clear trend indicating greater attention to this topic within our period of review. Among all eligible sources, the proportion addressing legal and policy issues broadly has shown a declining trend in recent years. There was an uptick in the number of sources discussing legal liability, patient acceptability, and clinician acceptability. Yet, these three topics remained infrequently addressed overall. Significant gaps in research on the ethics of AI applications in healthcare include disclosure of results to patients, legal liability, and patient and clinician acceptability. Future research should focus more on these ethical issues to facilitate the responsible and appropriate implementation of AI in healthcare.
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.