ReviewInternal medicine journal2025
Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare.
Review in Internal medicine journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Adaptive Optics Image Analysis Using Generative AI (GPT-4) Scripting: Exploratory Study.JMIR formative research · 2026Article
- Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators.NPJ digital medicine · 2026Article
- Ethical challenges in proxy online health information seeking by nursing interns for patients: a qualitative interview study.BMC medical ethics · 2026Article
- The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students.Frontiers in public health · 2025Article
- Evaluating artificial intelligence-generated multiple-choice questions in clinical pathology.Academic pathologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (GenAI) is a form of deep learning AI based on inference that offers significant potential in healthcare. It has versatile capabilities: GenAI excels in complex human language communication, synthesising information from large and diverse datasets and performing broad, complex tasks reliably. Other important capabilities include scalability, 'always on' and cost effectiveness. Taken together, GenAI technology appears to possess considerable potential for healthcare. However, the implementation poses several challenges, including technological problems, regulatory considerations, workforce impact and building trust. Using evidence and expert opinion to explore these issues, the review aims to inform clinical experts about this rapidly evolving field.
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.