Transparency in HealthTech: Unveiling the Power of Explainable AI

Abstract

Healthcare is a dynamic and intricate field that encompasses preventing, diagnosing, treating, and managing illnesses and injuries, striving to improve the overall well-being of individuals and communities. In the modern era, technological advancements have revolutionized healthcare, paving the way for innovative solutions to enhance patient care, optimize operational processes, and aid medical professionals in making informed decisions. Artificial intelligence (AI) has emerged as a pivotal force, offering unparalleled opportunities to transform healthcare delivery. However, the complex algorithms that power AI models have raised concerns, especially in critical areas like healthcare, where understanding the rationale behind decisions is paramount. This challenge finds its solution in Explainable AI (XAI), a paradigm that brings transparency and interpretability to AI systems, ensuring that healthcare providers and patients can grasp the logic governing AI-generated outcomes. AI has a vast scope in healthcare, ranging from predicting diseases like lung cancer, brain cancer, etc. to accelerating drug discovery and optimizing treatment procedures. Predictive models powered by AI analyze diverse datasets to identify patterns indicative of diseases. Early detection through AI-driven algorithms significantly augments the chances of successful treatment, underscoring the importance of AI in healthcare. The core of this chapter delves into XAI, elucidating its significance in ensuring the accountability and transparency of AI models. Techniques like local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) are explored to demystify AI predictions. Through its locally faithful explanations, LIME provides understandable insights into individual predictions, elucidating complex AI decisions for healthcare professionals.

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