Applied IT & Engineering
Information and engineering sciences | Online ISSN 3068-0115
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Volume 2 Number 1 2024
REVIEWS (Open Access)
Interpretable AI in EHR-Based Clinical Decision Support: A Scoping Review of Models, Methods, and Trends
Md Tanzimul Islam1*, Md Shahriar Masud1, Sudip Saha2
Applied IT & Engineering 2 (1) 1-11 https://doi.org/10.25163/engineering.2110817
Submitted: 12 July 2024 Revised: 09 September 2024 Accepted: 16 September 2024 Published: 17 September 2024
Abstract
Background: Electronic Health Records (EHRs) generate vast volumes of structured and unstructured patient data, and artificial intelligence (AI) is increasingly proposed to convert that data into actionable clinical insight. Yet the opacity of many AI models — their so-called "black box" character — continues to limit clinician trust and real-world adoption, which is precisely why interpretable, or explainable, AI has drawn so much recent attention.
Aims: This scoping review set out to map how AI models and interpretability methods have been applied within EHR-based Clinical Decision Support Systems (CDSS), and to characterize the clinical domains, countries, and outcomes represented in this literature.
Methods: Following PRISMA-ScR guidance, we searched PubMed, Scopus, Web of Science, and IEEE Xplore for peer-reviewed studies published between 2018 and 2023. After screening, 36 studies met inclusion criteria and were charted for AI model type, interpretability method, clinical domain, country, sample size, and reported outcome.
Results: Neural networks were the most frequently applied model (13 studies), followed by random forest (9), XGBoost (8), and logistic regression (6). SHAP was the dominant interpretability method (15 studies), ahead of attention-based visualization (12) and LIME (9). Cardiology was the most studied domain (10 studies), and contributions were evenly spread across the USA, Canada, the UK, India, Germany, and Australia (6 each). Sample sizes ranged from 430 to 1,350 patients.
Conclusion: Interpretable AI is steadily, if unevenly, being woven into EHR-based CDSS research. Closing the gap in underrepresented domains and standardizing how interpretability itself is evaluated remain the field's clearest next steps.
Keywords: Interpretable Artificial Intelligence; Explainable AI; Electronic Health Records; Clinical Decision Support Systems; Scoping Review
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