Explainable Artificial Intelligence in Healthcare: Progress, Challenges, and Future Directions
TL;DR
This review provides a comprehensive overview of recent developments in XAI for healthcare, focusing on key methods for achieving interpretability, such as intrinsically interpretable models, and post-hoc explanation methods such as SHAP, LIME, Grad-CAM, attention mechanisms, surrogate models, and counterfactual explanations.
Abstract
This study aims to illustrate the significance of Abstract Science Intelligence (XAI) in the medical field, outlining its role in enhancing the interpretability of traditional "black-box" AI models.The goal of this research is to shed light on the significance of Abstract Science Intelligence (XAI) in the medical sector, elucidating its function in bolstering the interpretability of conventional "black-box" Artificial Intelligence models. In medical imaging, disease diagnosis, clinical decision support, and precision medicine, deep learning techniques have proven to be extremely successful; however, the inability to explain and understand how deep learning models work raises a variety of challenges for clinician trust, patient safety, ethical accountability, and regulatory compliance. This review provides a comprehensive overview of recent developments in XAI for healthcare, focusing on key methods for achieving interpretability, such as intrinsically interpretable models, and post-hoc explanation methods such as SHAP, LIME, Grad-CAM, attention mechanisms, surrogate models, and counterfactual explanations. A detailed review of the use of these methods in a variety of clinical areas such as radiology, oncology, cardiology, genomics, electronic health records and drug discovery is also given. Furthermore, the paper examines technical issues concerning explanation fidelity, computational complexity, robustness, scalability, and model validation, as well as ethical issues such as fairness, transparency, privacy, bias, and governance. Other research trends are also discussed, such as causal explainability, humancentered XAI, models that are uncertain, human-in-the-loop systems, and evaluation frameworks. In summary, the review highlights that explainability is not just a technical aspect but a key component in creating AI systems that are both trustworthy and clinically sound and can assist in safe and effective health care decision-making.