Current Status, Challenges, and Future Prospects of Artificial Intelligence in Medicine

Abstract

With its enormous potential and value, artificial intelligence (AI) technology is being incorporated into every facet of the medical industry at a never-before-seen pace, from basic research to clinical practice to public health management. This study thoroughly examines the development of AI applications in several necessary fields, such as emergency medicine, medical research, treatment planning, disease diagnosis and typing, medical device regulation, and patient management. It focuses on how fundamental technologies like computer vision (CV), natural language processing (NLP), deep learning (DL), and machine learning (ML) can propel medical advancements in areas like image analysis, genomics data integration, assisted decision making, and process optimization. The study also highlights AI's central issues in medical applications, such as model interpretability and data privacy and quality, ethical rules, algorithmic bias, and clinical translation obstacles. To achieve the safe, efficient, and responsible deployment and application of AI technologies in medicine—which will ultimately enable precision medicine and improved human health—it concludes by offering an outlook on the future direction of development, highlighting the necessity of interdisciplinary collaboration, bolstering regulatory science, encouraging data standardization, and investigating explainable AI (XAI).