The healthcare industry is undergoing a rapid transformation thanks to artificial intelligence (AI), which is boosting treatment planning, increasing diagnostic accuracy, and streamlining hospital administration processes. AI tools like computer vision, machine learning, deep learning, and natural language processing make it possible to analyse vast amounts of medical data, including genomic information, medical imaging, and electronic health records[1]. Google DeepMind and IBM Watson Health are two examples of advanced AI models that have shown the potential of intelligent systems in clinical decision assistance, medication discovery, and disease prediction. In order to improve patient outcomes and lower death rates, these technologies let medical personnel identify diseases including diabetes, cancer, and cardiovascular ailments early on[2].Beyond clinical settings, AI is important for administrative automation, robotic surgery, telemedicine, and personalised medicine. While predictive analytics enhances resource allocation and lowers hospital operating expenses, chatbots and virtual assistants driven by AI assist in remote patient monitoring. In times of global health emergencies such as the COVID-19 pandemic, AI-based technologies were extensively employed for contact tracing, outbreak prediction, and vaccine development assistance[3]. Notwithstanding its many advantages, there are still important problems that need to be resolved, including data privacy, algorithm bias, ethical dilemmas, and regulatory compliance. The use of AI in healthcare is, all things considered, a revolutionary strategy that improves the effectiveness, precision, and accessibility of medical services while opening the door to a more intelligent and patient-focused healthcare system[4].
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Healthcare Technology, Medical Imaging Analysis, Predictive Analytics, Precision Medicine, Robotic Surgery
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