# The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review

arXiv: https://arxiv.org/abs/2409.07347 · HTML: https://mlsanity.com/paper/2409.07347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 226 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 30 of 32)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Explainable AI: current status and future directions](https://mlsanity.com/paper/2107.07045/) (2021)
- [Resolving challenges in deep learning-based analyses of histopathological images using explanation methods](https://mlsanity.com/paper/1908.06943/) (2019)
- [An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI](https://mlsanity.com/paper/2008.04024/) (2020)
- [An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery](https://mlsanity.com/paper/2008.00238/) (2020)
- [Interpretable Deep Neural Networks for Single-Trial EEG Classification](https://mlsanity.com/paper/1604.08201/) (2016)
- [Explaining the Unique Nature of Individual Gait Patterns with Deep Learning](https://mlsanity.com/paper/1808.04308/) (2018)
- [Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria](https://mlsanity.com/paper/2211.01738/) (2022)
- [Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence](https://mlsanity.com/paper/2108.10717/) (2021)
- [Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies](https://mlsanity.com/paper/2207.11352/) (2022)
- [Predicting post-operative right ventricular failure using video-based deep learning](https://mlsanity.com/paper/2103.00364/) (2021)
- [An Explainable-AI approach for Diagnosis of COVID-19 using MALDI-ToF Mass Spectrometry](https://mlsanity.com/paper/2109.14099/) (2021)
- [Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones](https://mlsanity.com/paper/2103.09171/) (2021)
- [Toward Knowledge-Driven Speech-Based Models of Depression: Leveraging Spectrotemporal Variations in Speech Vowels](https://mlsanity.com/paper/2210.02527/) (2022)
- [Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI](https://mlsanity.com/paper/2211.06238/) (2022)
- [Feature Importance versus Feature Influence and What It Signifies for Explainable AI](https://mlsanity.com/paper/2308.03589/) (2023)

## Abstract (arXiv)

The complex nature of disease mechanisms and the variability of patient symptoms pose significant challenges in developing effective diagnostic tools. Although machine learning (ML) has made substantial advances in medical diagnosis, the decision-making processes of these models often lack transparency, potentially jeopardizing patient outcomes. This review aims to highlight the role of Explainable AI (XAI) in addressing the interpretability issues of ML models in healthcare, with a focus on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. A comprehensive literature search was conducted across multiple databases to identify studies that applied XAI techniques in healthcare. The search focused on XAI algorithms used in diagnosing and monitoring chronic diseases. The review identified the application of nine trending XAI algorithms, each evaluated for their advantages and limitations in various healthcare contexts. The findings underscore the importance of transparency in ML models, which is crucial for improving trust and outcomes in clinical practice. While XAI provides significant potential to bridge the gap between complex ML models and clinical practice, challenges such as scalability, validation, and clinician acceptance remain. The review also highlights areas requiring further research, particularly in integrating XAI into healthcare systems. The study concludes that XAI methods offer a promising path forward for enhancing human health monitoring and patient care, though significant challenges must be addressed to fully realize their potential in clinical settings.