Core Concepts in Pharmacoepidemiology: Principled Use of Artificial Intelligence and Machine Learning in Pharmacoepidemiology and Healthcare Research

Le résumé fourni par la source

Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these technologies within healthcare settings. AI/ML-based tools also play a role in pharmacoepidemiology research, as we may apply them to answer our own research questions, take responsibility for evaluating medical devices with AI/ML components, or participate in interdisciplinary research to create new AI/ML algorithms. While epidemiologic expertise is essential to deploying AI/ML responsibly and ethically, the rapid advancement of these technologies in the past decade has resulted in a knowledge gap for many in the field. This article provides a brief overview of core AI/ML concepts, followed by a discussion of potential applications of AI/ML in pharmacoepidemiology research, and closes with a review of important concepts across application areas, including interpretability and fairness. This review is intended to provide an accessible, practical overview of AI/ML for pharmacoepidemiology research, with references to further, more detailed resources on fundamental topics.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.