ArticleNPJ digital medicine2025
Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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The trial behind it
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Who cites it
33 citing papers in PubMed.
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- Development and validation of an explainable machine learning model for risk stratification in patients with clinically suspected acute pulmonary embolism: retrospective study.Clinics (Sao Paulo, Brazil) · 2026Article
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- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Explainable Machine Learning Reveals Pattern-Specific Drivers of Depression in Middle-Aged and Older Adults with Multimorbidity: A Nationwide Cross-Sectional Study from CHARLS.Healthcare (Basel, Switzerland) · 2026Article
- Computational interpretation of modified lung ultrasound performance in CTD-ILD.Clinical rheumatology · 2026Article
- A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Exploring tuberculosis physicians' preferences for AI explainability in China: a protocol for a discrete choice experiment.BMJ open · 2026Article
- Development and internal validation of a clinical prediction model for postoperative urinary tract infection in older surgical patients: a retrospective cohort study.BMC geriatrics · 2026Article
- DCBM-Tri: a dual-channel bilinear mapping triplet model for early recognition of acute kidney injury in imbalanced cohorts.Scientific reports · 2026Article
- Development and external validation of the HCH and HPMS prognostic indices for sepsis: a retrospective model development study using a Multi-Objective Non-Newtonian Fluid optimization algorithm.BMC medical informatics and decision making · 2026Article
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- Implementation of an AI-Based Clinical Decision Support System Predicting In-Hospital Cardiac Arrest in General Wards: A Multicenter Staggered-Implementation Study in Secondary Hospitals in Korea.Diagnostics (Basel, Switzerland) · 2026Article
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Early prediction of transfusion requirements in trauma patients using explainable machine learning.Transfusion · 2026Article
- Development and prospective shadow evaluation of a domain-specific large language model for emergency neurological diagnosis.NPJ digital medicine · 2026Article
- Explainable machine learning with routine biomarkers identifies culture-defined bacteremic urosepsis.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Clinical decision-making substantially impacts patients' lives and their quality of life. However, the black-box nature of AI-powered clinical decision support systems (CDSSs) complicates the interpretation of how decisions are derived. Explainable AI (XAI) improves acceptance and trust with explanations, but the effectiveness of different methods remains uncertain. We compared the acceptance, trust, satisfaction and usability of various explanatory methods among clinicians. We also explored the factors associated with acceptance levels for each item using trust, satisfaction and usability score questionnaires. Surgeons and physicians (N = 63), who had prescribed blood products before surgery, made decisions before and after receiving one of three CDSS explanation methods, each comprising six vignettes, in a counterbalanced design. We found empirical evidence, which indicates that providing a clinical explanation enhances clinicians' acceptance than presenting 'results only' or 'results with SHapley Additive exPlanations (SHAP)'. Additionally, trust, satisfaction and usability were correlated with acceptance. This study suggests best practices for the strategic application of the XAI-CDSS in the medical field.
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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.