JMIR Publications

ABSTRACT

Adverse drug reactions (ADR) are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System (FAERS) as part of its surveillance activities. Over the past decade, FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. A gap remains between AI algorithm development and deployment. We apply Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at FDA were accepted by safety reviewers and others were not. Two key lessons stand out. First, the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts. Second, the process by which clinicians decide from case reports whether a drug is likely to cause an adverse event is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an adverse event. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.