Artificial intelligence and machine learning in drug utilization research
Maurizio Sessa, Saeed Shakibfar, Morten Tolstrup Andersen, Jing Zhao · 2024
This chapter provides an overview of the fundamentals of artificial intelligence while describing the use of knowledge discovery techniques in drug utilization research along with examples, privacy/technical/analytical/ethical considerations, and future perspectives. Among artificial intelligence subfields, machine learning has emerged as one of the most prominent in biomedicine, mostly due to the considerable advancement in computer technologies and impressive achievements in learning algorithms. A big contribution to the learning abilities of machine learning has been given by the rapid increase in the quantity, diversity, and accessibility of big data. The increased availability, quantity, diversity, and accessibility of large databases, accompanied by the evolution of technology and analytical techniques in artificial intelligence, has created considerable technical, analytical, privacy, and ethical challenges for drug utilization research. During artificial intelligence processing of personal data, data persistence, repurposing, and spillovers may lead to privacy issues.