ArticleJAMIA open2026
Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- Recent advances in screening, diagnosis, prognosis and personalized treatment of sepsis.European journal of microbiology & immunology · 2026Review
- The Sepsis ImmunoScore Predicts Sepsis, Mortality, and Deterioration Better than Clinical Scores and Widely Available Biomarkers.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
Corrections and comments
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Authors and funding
51 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To assess the interpretability and acceptance of Shapley values for making artificial intelligence/machine learning (AI/ML) tools more transparent, interpretable, and useful to clinicians. Materials and Methods: Structured assessments were conducted with 30 clinicians (15 providers; 15 nurses; 8 assessments per clinician) to evaluate their ability to understand interventional Shapley Additive exPlanations (SHAP) values, a type of Shapley value that provides individualized variable importance scores and ascertain their perspective on SHAP value utility for the use of an AI/ML sepsis diagnostic. Participants were shown the diagnostic interface for real clinical scenarios with de-identified patient data with and without SHAP values. The primary outcomes were clinician ability to correctly interpret SHAP values and clinician self-reported improvement in their understanding of how the AI/ML algorithm produced its result. Results: Participants correctly interpreted SHAP values in 235 of 240 assessments (98%; CI, 95%-99%) and reported SHAP values improved their understanding of how the algorithm produced its result in every case (240/240; 100%; CI, 99%-100%). Participants were unanimous (30/30) in preferring the interface with SHAP values over the interface without. Discussion: Clinician participants strongly preferred the device interface with SHAP values, were unanimous in reporting SHAP values improved their understanding of the AI/ML diagnostic, and scored nearly perfectly when asked to interpret SHAP values. Conclusion: These results suggest health care providers value transparency into AI/ML algorithms designed for clinical use, and that Shapley values are a useful approach to providing that transparency, which in turn may improve tool adoption and clinical utility.
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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.