ArticleJournal of the American Medical Informatics Association : JAMIA2025
Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Evaluation of prostate cancer pathology reports generated by ChatGPT to enhance patient comprehension.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
introductionThe rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts. APPROACH: In response to this need, the National Academy of Medicine (NAM) has initiated the development of an AI code of conduct (AICC) through its Digital Health Action Collaborative. This code of conduct is grounded in shared principles and commitments, aiming to actualize ethical and effective AI practices within the broader health and healthcare ecosystem. Given its specialized expertise and insight, the biomedical informatics (BMI) community plays a pivotal role in shaping and applying these guidelines. RECOMMENDATIONS: We, as members of the AICC Steering Committee and the NAM Digital Health Action Collaborative, urge BMI educators, researchers, and practitioners to engage actively in refining and implementing the AICC. This involvement is critical to ensuring that the code is robust, applicable, and continuously improved to meet the evolving challenges facing health and healthcare.
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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.