Demystifying artificial intelligence in pharmacy

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Purpose</jats:title> <jats:p>To provide pharmacists and other clinicians with a basic understanding of the underlying principles and practical applications of artificial intelligence (AI) in the medication-use process.</jats:p> </jats:sec> <jats:sec> <jats:title>Summary</jats:title> <jats:p>“Artificial intelligence” is a general term used to describe the theory and development of computer systems to perform tasks that normally would require human cognition, such as perception, language understanding, reasoning, learning, planning, and problem solving. Following the fundamental theorem of informatics, a better term for AI would be “augmented intelligence,” or leveraging the strengths of computers and the strengths of clinicians together to obtain improved outcomes for patients. Understanding the vocabulary of and methods used in AI will help clinicians productively communicate with data scientists to collaborate on developing models that augment patient care. This primer includes discussion of approaches to identifying problems in practice that could benefit from application of AI and those that would not, as well as methods of training, validating, implementing, evaluating, and maintaining AI models. Some key limitations of AI related to the medication-use process are also discussed.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>As medication-use domain experts, pharmacists play a key role in developing and evaluating AI in healthcare. An understanding of the core concepts of AI is necessary to engage in collaboration with data scientists and critically evaluating its place in patient care, especially as clinical practice continues to evolve and develop.</jats:p> </jats:sec>

Detecting drug diversion in health-system data using machine learning and advanced analytics

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