ArticlePloS one2025
Exploring ethical considerations in medical research: Harnessing pre-generated transformers for AI-powered ethics discussions.
Article in PloS one, 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.
- Ethical Challenges in Research Involving Hospitalized Patients: A Phenomenological Study.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIn medical research involving human subjects, ethical review is essential to protect individuals. However, concerns have been raised about variations in ethical review opinions and a decline in review quality. Adequately protecting human subjects requires multifaceted opinions from ethics committee members. Despite the need to increase the number of committee members, resources are limited. To address these challenges, we explored the use of a generative pre- learning transformer, an interactive artificial intelligence (AI) tool, to discuss ethical issues in medical research.
methodsThe generation AI used in the research used ChatGPT3.5, which has learned ethical guidelines from various countries worldwide. We requested the generative AI to provide insights on ethical considerations for virtual research involving individuals. The obtained answers were documented and verified by experts.
resultsThe AI successfully highlighted considerations for informed consent regarding individuals with dementia and mental illness, as well as concerns about invasiveness in research. It also raised points about potential side effects of off-label drug use. However, it could not offer specific measures for psychological considerations or broader ethical issues, providing limited ethical insights. This limitation may be attributed to biased opinions resulting from machine learning optimization, preventing comprehensive identification of certain ethical issues.
conclusionAlthough the validity of ethical opinions generated by the generative AI requires further examination, our findings suggest that this technology could be employed to prompt reviews and re-evaluate ethical concerns arising in research.
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.