Example Draft: Explainable AI Methods in ICU Machine Learning - A Scoping Review

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Research Question

What explainable AI (XAI) methods have been applied to machine learning models used in intensive care unit (ICU) clinical decision support, and what are their reported effectiveness, limitations, and implementation challenges?

This scoping review follows the Arksey & O'Malley (2005) methodological framework and PRISMA-ScR guidelines to systematically map the landscape of explainability techniques applied to critical care AI systems, from SHAP and LIME to attention mechanisms and concept-based explanations.

Abstract

The integration of deep learning into critical care medicine offers unprecedented opportunities for predicting adverse patient outcomes, yet the opacity of these "black box" algorithms presents a fundamental barrier to clinical adoption. In high-stakes environments where decisions determine life or death, the lack of algorithmic transparency raises significant concerns regarding safety, clinician trust, and ethical accountability.

This thesis presents a comprehensive scoping review of Explainable AI (XAI) methods applied within the Intensive Care Unit (ICU), investigating how technical interpretability techniques are being translated into clinical practice. The analysis reveals that while advanced neural networks outperform traditional scoring systems like APACHE and SOFA, an inverse relationship often persists between predictive performance and interpretability.

XAI Methods Covered

Clinical Applications Reviewed

• Mortality Prediction - APACHE and SOFA-based ML models with XAI overlays
• Sepsis Early Warning - Real-time sepsis detection with interpretable alerts
• Acute Kidney Injury (AKI) - Predictive models with feature explanations
• Mechanical Ventilation - Weaning decisions and ventilator settings
• Hypotension Prediction - Intraoperative blood pressure forecasting
• Drug Dosing - Vasopressor and sedation recommendations

Key Contributions

1. A comprehensive taxonomy of existing XAI applications in critical care settings, distinguishing between technical explainability and practical clinical utility
2. An evaluation of the mediating role of trust in Human-AI interaction, highlighting how lack of transparency risks causing either "blind trust" or rejection of valid alerts
3. An identification of critical gaps in safety validation, demonstrating that current regulatory frameworks require more robust specifications for algorithmic transparency

Citation Sample

All 50 citations in this scoping review are verified against academic databases. Here are some examples:

• Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology.
• Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint.
• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. NeurIPS.
• Tricco, A. C., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR). Annals of Internal Medicine.

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