ArticleJMIR research protocols2026
Understanding Responsible Development in AI-Based Clinical Prediction Models for Mortality: Protocol for a Scoping Review.
Article in JMIR research protocols, 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
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Prognostic inequity has been identified as a barrier to accessing end-of-life care for underrepresented groups. Artificial intelligence-based clinical prediction models (AIPMs) for prognostication of mortality have the potential to offer rapid, accessible, and accurate predictions that could streamline care. However, they may also exacerbate preexisting inequities in the health care system rather than address accessibility and quality. This can be caused by erroneous outputs from biased training data, outcomes from out-of-scope operationalization, and inexplicability due to opacity. Objective: The goal of this study is to synthesize peer-reviewed literature on the creation and application of AIPMs to prognosticate mortality in acute care settings for adult patients, offering new insights into responsible and ethical model development. Methods: A transdisciplinary, structured search strategy was developed in consultation with librarians from both health sciences and engineering sciences. The academic databases queried were Medline, Embase, IEEE Xplore, ACM Digital Library, Compendex, and Scopus. The search was conducted in spring 2025, and the results were uploaded to Covidence. A team of reviewers will screen in 2 rounds: titles and abstracts, then full texts. Eligibility will be determined by publication in academic journals or as full-length conference proceedings, language, model output, and AI use. Data will be charted using adapted charting tools and then analyzed by descriptive, summary, and qualitative synthesis. Results: The search was completed on March 25, 2025, with screening starting in May 2025. Results are anticipated for January 2026. Conclusions: This review will provide a comprehensive summary of AIPMs that predict mortality, highlighting the specific elements included in their development. Informed by the responsible research and innovation (RRI) framework, we will consider interest-holder engagement, interdisciplinary collaboration, and computational and clinical ethics will in the context of the four RRI dimensions: anticipation, reflexivity, inclusion, and responsiveness.
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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.