The Potential Role of Artificial Intelligence in Systematic Follow-Up Recommendation Tracking and Outcome Assessment.

Abstract

Actionable findings requiring follow-up with additional imaging or other diagnostic procedures are frequently reported for a wide variety of radiology examinations. Completion of recommended follow-up can lead to new diagnoses including cancer. However, recommended follow-up completion is inconsistent, particularly when follow-up is for findings unrelated to the initial reason for the examination. Follow-up recommendation tracking systems, using a combination of information technology tools and human navigators, can facilitate completion of recommended follow-up, but often require significant effort for manual chart review and direct communication with providers and patients. Artificial intelligence, including large language models able to process vast and diverse unstructured text data, offer the opportunity to improve efficiency with data extraction and aggregation tasks, like those required for follow-up recommendation management. In this review article, we will review the key components of follow-up recommendation management systems: (1) identification of follow-up recommendations within radiology reports, (2) communication of these recommendations, (3) tracking of follow-up recommendations to completion, and (4) outcomes tracking. For each component, we will explore how artificial intelligence can improve efficiency and expand capabilities of robust management systems that ensure the loop is closed for follow-up recommendations.