ArticleDigital health
The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Electronic health records: managerial insights from an umbrella review.BMC health services research · 2026Pooled it
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Machine learning in the diagnosis and prognosis of transient ischaemic attack: a systematic review.BMC neurology · 2026Pooled it
- Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.International urology and nephrology · 2026Observational
- Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis.Health care science · 2026Review
- Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing.Scientific reports · 2026Article
- Development and validation of a generalisable machine learning algorithm for identifying interstitial lung disease cohorts: a retrospective cohort study.EClinicalMedicine · 2026Article
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- AI-driven drug reposition for pathogens: a new paradigm in pandemic preparedness.Frontiers in chemistry · 2026Review
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Enhancing sarcopenia screening in primary care: a machine learning approach using simple physical tests vs. SARC-F in 2,788 community-dwelling older adults.Frontiers in public health · 2026Article
- Interpretable Machine Learning Models Based on Blood Cell-Derived Inflammatory Indices for Identifying Colorectal Neoplasia: A Retrospective Study.Journal of inflammation research · 2026Article
- An interpretable machine learning model for detecting vision-threatening diabetic retinopathy among patients with diabetic retinopathy: a web-based cross-sectional study.Frontiers in endocrinology · 2026Article
- A consensus-weighted multi-agent ensemble feature selection framework for antibiotic susceptibility prediction in pediatric respiratory diseases.Frontiers in artificial intelligence · 2026Article
- Application of explainable artificial intelligence integrating with electronic health record in oncology.Exploration of targeted anti-tumor therapy · 2026Review
- Predicting Multidrug-Resistant Pneumonia: An Interpretable Machine Learning Model Validated in US and Chinese Patient Cohorts.Infection and drug resistance · 2026Article
- Early autism detection: a review of emerging technologies, biomarkers, and explainable AI approaches.Molecular brain · 2025Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Machine Learning (ML) and Deep Learning (DL) models show potential in surpassing traditional methods including generalised linear models for healthcare predictions, particularly with large, complex datasets. However, low interpretability hinders practical implementation. To address this, Explainable Artificial Intelligence (XAI) methods are proposed, but a comprehensive evaluation of their effectiveness is currently limited. The aim of this scoping review is to critically appraise the application of XAI methods in ML/DL models using Electronic Health Record (EHR) data. In accordance with PRISMA scoping review guidelines, the study searched PUBMED and OVID/MEDLINE (including EMBASE) for publications related to tabular EHR data that employed ML/DL models with XAI. Out of 3220 identified publications, 76 were included. The selected publications published between February 2017 and June 2023, demonstrated an exponential increase over time. Extreme Gradient Boosting and Random Forest models were the most frequently used ML/DL methods, with 51 and 50 publications, respectively. Among XAI methods, Shapley Additive Explanations (SHAP) was predominant in 63 out of 76 publications, followed by partial dependence plots (PDPs) in 11 publications, and Locally Interpretable Model-Agnostic Explanations (LIME) in 8 publications. Despite the growing adoption of XAI methods, their applications varied widely and lacked critical evaluation. This review identifies the increasing use of XAI in tabular EHR research and highlights a deficiency in the reporting of methods and a lack of critical appraisal of validity and robustness. The study emphasises the need for further evaluation of XAI methods and underscores the importance of cautious implementation and interpretation in healthcare settings.
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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.