Explainable AI in Healthcare

Abstract

With the advent of machine learning (ML)-based tools in the healthcare domain, various treatment methodologies like digital healthcare (HC) by integrating cross domain fusion from cross-modality imaging and non-imaging of health data and personalized treatments have been recommended to improve the overall efficacy of the healthcare systems. Due to the intensive need of skilled physicians to combat with the as the extraneous strength, the advantages of ML approaches include a larger range of functionalities such as filtering emails, identifying objects in images and analysing large volumes of complex interrelated data. It is observed that the massive amounts of healthcare data which have been generated everyday within electronic health records. In turn, the healthcare providers take a more predictive approach to come out with a more unified system which concentrates on clinical decision support, clinical practice development guidelines, and automated healthcare systems, thereby offering a broad range of features in precise manner such as improving patient data for better diagnosis, medical research for future references. This chapter provides a complete overview of a typical ML workflow comprises the predominant phases, namely data collection, data pre-processing, modelling, training, evaluation, tuning, and deployment, and the role of explainable artificial intelligence (XAI) mechanisms assists to integrate interoperability and explainability into the ML workflow. In general, XAI can be defined as the set of processes and methods that produces details or comprehensive justifications pertaining to the functioning of the model or easy to understand and trust the potential outcomes generated by ML techniques. The ultimate aim lies in explaining the interaction to the end user leads to a trustworthy environment. In addition to that, XAI assures the privileges with regard to the healthcare domain are dimension reduction, feature importance, attention mechanism, knowledge distillation, surrogate representations used to develop and validate a decision supporting tool using XAI. The positive growth of XAI nuanced the wider usage of aggregated, personalized health data to generate with ML models for diagnosis automation, prompt, and precise way of tailoring therapies with optimality and in a dynamic manner. XAI mechanisms ensure better decision making by letting the end-user know how the ML model derived the potential outcomes and medical results.