AI-Enhanced Clinical Decision-Making Through a Collaborative Approach

Abstract

AI integration in healthcare transforms medical decision-making and treatment processes, enhancing patient care and advancing research. Deep Q-Networks (DQNs) with reinforcement learning help healthcare professionals analyze data for personalized treatment plans. AI automation boosts diagnostic accuracy and operational efficiency through collaboration between humans and machines. This chapter focuses on AI-powered clinical decision support systems that assist in diagnosing complex diseases and developing treatment strategies. Real-time analysis of medical imaging and health records enables early detection of cancers, cardiovascular disorders, and neurological disorders. Reinforcement learning also streamlines hospital resource use and cuts costs. Despite progress, ethical oversight is essential to address privacy, algorithmic bias, and ensure transparency. This study explores human-AI synergy in clinical settings, pointing out the importance of trustworthy, ethically guided AI that complements human intuition for improved patient outcomes.