Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings

As the implementation of artificial intelligence (AI)-enabled tools is realized across diverse clinical environments, there is a growing understanding of the need for ongoing monitoring and updating of prediction models. Dataset shift-temporal changes in clinical practice, patient populations, and information systems-is now well-documented as a source of deteriorating model accuracy and a challenge to the sustainability of AI-enabled tools in clinical care. While best practices are well-established for training and validating new models, there has been limited work developing best practices for prospective validation and model maintenance. In this paper, we highlight the need for updating clinical prediction models and discuss open questions regarding this critical aspect of the AI modeling lifecycle in three focus areas: model maintenance policies, performance monitoring perspectives, and model updating strategies. With the increasing adoption of AI-enabled tools, the need for such best practices must be addressed and incorporated into new and existing implementations. This commentary aims to encourage conversation and motivate additional research across clinical and data science stakeholders.

Automated summary

As the adoption of AI-enabled tools increases in clinical settings, the importance of ongoing monitoring and updating of prediction models becomes evident. Changes in clinical practice, patient populations, and information systems can result in deteriorating model accuracy, posing a challenge to the sustainability of AI-enabled tools in clinical care. This paper emphasizes the need for updating clinical prediction models and discusses open questions in model maintenance policies, performance monitoring perspectives, and model updating strategies.