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Explainable AI methods reviewed for clinical research applications

By PulseAugur Editorial · [1 sources] ·

This paper provides a structured review of Explainable Machine Learning (XML) methodologies, detailing global and local interpretability tools like SHAP, LIME, PDP, and ICE plots. It explains the mechanisms, outputs, and limitations of each method, using a Heart Disease Dataset to demonstrate their application. The review highlights how XML techniques offer insights into predictor influence, identify nonlinear relationships and interactions, and reveal patient-level risk heterogeneity, ultimately supporting more transparent and accountable ML applications in clinical research. AI

IMPACT Provides a methodological primer to bridge advanced ML techniques with clinical applicability, aiding transparent decision-making.

RANK_REASON The item is an academic paper published on arXiv detailing research methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Explainable AI methods reviewed for clinical research applications

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The item is an academic paper published on arXiv detailing research methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Krishna Padmanabhan, Minxin Lu, Dai Feng, Natalia KanDobrosky, Sai Konduri, Heather J. Litman, Achilleas Livieratos ·

    Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research

    arXiv:2608.07522v1 Announce Type: cross Abstract: We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic E…

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