Abstract Objectives To provide a practical and methodologically grounded overview of explainable machine learning (XML) approaches in healthcare, with emphasis on their interpretation and application in clinical research and decision support. By moving beyond traditional predictive models, this primer aims to foster trust, transparency, and informed clinical decision-making, ultimately bridging the gap between data science and medical practice. Materials and Methods We present a structured review of commonly used XML methodologies, including global and local interpretability tools such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots. For each method, we explain the underlying mechanism at a high level, visualize representative outputs, and provide structured guidance on interpretation, appropriate use, and limitations, illustrated using the publicly available Heart Disease dataset. Results XML techniques provided intuitive visual and quantitative insights into how predictors influence model predictions. Global methods characterized population-level feature effects, whereas local methods revealed patient-level contributions useful for individualized interpretation. Our worked examples demonstrate how XML outputs can identify nonlinear relationships, detect interaction effects, and reveal heterogeneity in predicted risk across patients, addressing key challenges in translating ML predictions into interpretable outputs for clinical research. Discussion and Conclusion XML tools offer valuable interpretability for ML models and support more transparent and accountable ML applications in clinical research. By providing a methodologically grounded overview alongside practical implementation examples and structured guidance on each method's strengths and limitations, this primer helps bridge the gap between advanced ML methodology and clinical applicability. Thoughtful adoption of XML approaches may facilitate better understanding, communication, and critical evaluation of ML predictions in healthcare research, ultimately supporting evidence-based clinical decision-making.
Padmanabhan et al. (Tue,) studied this question.