A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balance interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends, offering valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, fostering the development of transparent, trustworthy, and effective AI-driven solutions

Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

Drug discovery and development is a time-consuming process that involves identifying, designing, and testing new drugs to address critical medical needs. In recent years, machine learning (ML) has played a vital role in technological advancements and has shown promising results in various drug discovery and development stages. ML can be categorized into supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning is the most used category, helping organizations solve several real-world problems. This study presents a comprehensive survey of supervised learning algorithms in drug design and development, focusing on their learning process and succinct mathematical formulations, which are lacking in the literature. Additionally, the study discusses widely encountered challenges in applying supervised learning for drug discovery and potential solutions. This study will be beneficial to researchers and practitioners in the pharmaceutical industry as it provides a simplified yet comprehensive review of the main concepts, algorithms, challenges, and prospects in supervised learning