Machine Learning in Healthcare

Abstract

Machine learning approaches are utilized in healthcare for computational decision-making in cases where critical medical data analysis is required to identify hidden linkages or anomalies that are not evident to humans. Artificial intelligence (AI) tools can assess a wide range of health data; patient data from multi-omics methods; clinical, behavioural, environmental, pharmacological data; and data from the biomedical literature to respond to research issues that necessitate a big sample size on a difficult-to-reach population. In healthcare, digitising health data has eased the development of computational models and AI systems to extract insights from the data. This chapter initially addressed the prospectus of machine learning in public health with significant focus areas. The medical devices and equipment section contain device-based modelling approaches to various diseases. The chapter also includes brief details on chatbots, wearable technologies, drug distribution systems, vending machines, and text recognition from prescriptions and medicine boxes are addressed.