Leveraging machine learning and AI in healthcare: A paradigm shift from the traditional approaches

Electronic health data is becoming more and more accessible, which presents tremendous opportunities for medical research and development as well as useful advancements. Healthcare epidemiologists need computational methods that can handle big, complicated datasets in order to fully utilize these data. These tools are provided by machine learning (ML), which recognizes patterns that can change patient risk stratification, particularly in infectious diseases, and result in focused treatments that stop the spread of pathogens. Health emergencies and disease states can now be predicted more accurately thanks to recent developments in AI and ML. Although there is doubt about ML's usefulness in healthcare, its use is expanding quickly. In fields like radiology, genetics, and neuroimaging, machine learning techniques—including supervised, unsupervised, and reinforcement learning—have demonstrated efficacy. Nevertheless, issues like privacy and morality still need to be taken into account for applications in the future.