Applications of Artificial Intelligence in Healthcare: Opportunities, Challenges and Future Directions

Artificial intelligence (AI) has moved from experimental prototypes to real-world applications across the healthcare continuum, enabled by advances in machine learning, deep learning, data availability and computing infrastructure.[۱,۲] This paper provides a concise review of the major applications of AI in healthcare, including clinical diagnosis and decision support, medical imaging and pathology, personalized and predictive medicine, robotics and surgery, patient-facing tools and public health surveillance.[۱–۴] We summarize evidence from recent systematic reviews and high-impact studies to illustrate where AI systems have achieved performance comparable to or exceeding that of clinicians, for example in image-based cancer detection and risk prediction models, while also highlighting limitations related to generalizability, safety, bias and workflow integration.[۲–۴] Drawing on international guidance, including the World Health Organization report on ethics and governance of AI for health, we outline key ethical, legal and social challenges such as transparency, accountability, data protection and equity.[۵] The paper concludes with practical implications for clinicians, health-system leaders and regulators, emphasizing the need for rigorous evaluation, multidisciplinary collaboration and human-centred design to ensure that AI augments rather than undermines high-quality, trustworthy and equitable healthcare.[۱–۵]

کلیدواژه ها:

artificial intelligence ، machine learning ، deep learning ، healthcare ، decision support ، medical imaging ، personalized medicine ، ethics