Authors : Sai Mani Krishna Sistla; Vathsala Periyasamy; Jawaharbabu Jeyaraman

Volume/Issue : Volume 9 - 2024, Issue 2 - February

Google Scholar : https://tinyurl.com/35vps3ym

Scribd : https://tinyurl.com/2wckw9js

DOI : https://doi.org/10.5281/zenodo.10776780

Abstract : Explainable AI techniques are increasingly crucial in healthcare, where transparency and interpretability of artificial intelligence (AI) models are paramount. In domains like medical imaging and clinical decision-making, AI serves to elucidate the rationale behind AI-driven decisions, emulating human reasoning to bolster trust and acceptance. However, the implementation of XAI in healthcare is not without challenges, including algorithmic bias, operational speed, and the necessity for multidisciplinary collaboration to navigate technical, legal, medical, and patient-centric considerations effectively. By providing explanations to healthcare professionals, AI fosters trust and ensures the judicious use of AI tools. Overcoming issues such as bias in algorithmic outputs derived from data and interactions is essential for maintaining fairness in personalized medicine applications. Various XAI techniques, such as causal explanations and interactive interfaces, facilitate improved human-computer interactions by making AI decisions comprehensible and reliable. The development and deployment of XAI in clinical settings offer transparency to AI models but require concerted efforts to address practical concerns like speed, bias mitigation, and interdisciplinary cooperation to uphold the ethical and efficient utilization of AI in healthcare. Through the strategic application of XAI techniques, healthcare practitioners can leverage transparent and trustworthy AI systems to enhance decision-making processes and patient outcomes.

Keywords : Explainable AI (XAI), Healthcare, Radiomics, Human Judgment, Medical Imaging, Deep Learnings.