Learning to Heal

Abstract

Deep Q-Networks (DQNs) in smart healthcare enable artificial intelligence to rapidly alter medical decisions and learn from mistakes. DQNs build smart healthcare agents by leveraging reinforcement learning rather than rules. Dynamic learning advances diagnosis, treatment, and hospital operations. DQNs enable artificial intelligence systems to assess activities by using rewards and punishments, thereby optimizing their strategies. They draw on deep learning and Q-learning. Robotic-assisted procedures and ICUs save lives through educated judgments. DQNs identify diseases, develop drugs and tailored therapies. AI bots use patient data to tailor treatment. Problems abound. Think about morality, openness in decision-making, and data security. Explainable artificial intelligence in human-AI decision systems increases dependability and utility. As wearable's and telemedicine interfaces offer smart, responsive healthcare, DQNs also do. Human knowledge remains enormous. The DQNs should help physicians. Beyond data analysis, systems help enhance patient care.

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