International Journal of Science, Engineering and TechnologyAuthors: Vipul Kumar, Aashu Saini
Abstract: Artificial intelligence (AI) has emerged as a transformative technology in modern healthcare, demonstrating exceptional performance in diagnostics, prognosis, treatment planning, and patient monitoring. However, the deployment of AI in life-critical healthcare systems raises profound concerns regarding transparency, accountability, and trustworthiness. Black-box AI models, despite their high predictive accuracy, fail to provide clinicians with comprehensible reasoning, which is unacceptable in high-stakes medical environments. Explainable Artificial Intelligence (XAI) addresses this critical gap by making AI decision-making processes interpretable and transparent to human experts. This paper presents a comprehensive review of XAI methods applicable to life-critical healthcare systems, examining state-of-the-art techniques such as LIME, SHAP, Grad-CAM, attention mechanisms, and rule-based explanations. We discuss the unique challenges of applying XAI in critical care settings, including real-time explanation requirements, regulatory compliance, clinician trust, and ethical considerations. Furthermore, we explore current XAI applications in medical imaging, clinical decision support systems, ICU monitoring, and drug discovery. Our analysis reveals that while XAI holds immense promise, significant research gaps remain in achieving faithful, robust, and clinically meaningful explanations. We outline future research directions and propose a framework for evaluating XAI systems in healthcare environments.
