AI and Machine learning in Healthcare

The increasing adoption of electronic health records, IoT wearable products, imaging systems, and mobile health platforms have resulted in producing unwelcome amounts of clinical and behavioural data. Such a big-data setting has increased the pace at which artificial intelligence (AI) and machine learning (ML) are integrated into health care decision-making. Even with their potential, healthcare organisations have also been confronted with severe issues, such as the heterogeneity of data, privacy concerns, a lack of interpretability of black-box models, disparities in the performance of algorithms on disparate populations, and infrastructure barriers to real-time deployment. These constraints lower clinical trust and limit predictive analytics and remote monitoring systems based on AI. This study is intended to critically discuss the current application of AI and ML to healthcare, in particular, predictive analytics, IoT-based monitoring, smart hospital systems, big-data infrastructure, explainable AI models, and mobile health ecosystems, which is supported by evidence only of current research. This critical review is a flexible synthesis of empirical data to assess the quantifiable effects of AI on diagnostic accuracy, early detection, treatment optimisation, cost reduction and efficacy. It also explores the architectural enablers, including distributed learning frameworks and big-data pipelines, and deals with ongoing ethical, technical, and clinical issues. The article is a brief but detailed evaluation of the transformative effect of AI and its limitations in existing healthcare systems