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Kanigiri Kumari is an Assistant Professor in Electronics and Communication Engineering at Hyderabad Institute of Technology and Management, Hyderabad, Telangana, India. Her academic responsibilities include teaching and engineering-related scholarly activities. Her professional role contributes to higher education in electronics and communication engineering. Her contribution to this chapter focuses on explainable AI for disease prediction, diagnosis, and clinical decision support.
Artificial Intelligence (AI) has emerged as a significant area of investigation in healthcare, aiding in illness forecasting, diagnostic categorisation, risk evaluation, and clinical decision processes. Notwithstanding advancements in predictive modelling, the lack of transparency in intricate algorithms continues to pose a considerable obstacle to their implementation in clinical settings. XAI tackles these issues via feature attribution, interpretable models, counterfactual reasoning, visual explanation methods, uncertainty assessment, and human-centric decision-support systems. This chapter offers an extensive framework for creating transparent and dependable AI systems for illness forecasting, diagnostics, and clinical decision assistance. It underscores the necessity for systematic evaluation of explanations and the requirement for clinical utility to be confirmed via independent validation and prospective research prior to implementation.