Bridging the Gap - Translating AI in Pathology into Clinical Impact

AbstractHematoxylin and eosin-stained tissue analysis has remained the global foundation of diagnostic pathology since the 19th century, and recent advances in artificial intelligence (AI) now enable the extraction of clinical and molecular insights from routine images. Yet only 5 of the 1430 AI- and/or machine learning-enabled medical devices cleared by the U.S. Food and Drug Administration are modern pathology AI tools based on whole-slide images, whereas 1094 are classified in the radiology category. Furthermore, only approximately 4% of U.S. slide volume is currently read digitally. We argue that this disconnect reflects a hierarchy of barriers to clinical impact: foundational infrastructure and economic hurdles, postdigitization challenges related to sample variability and clinician trust, an underdeveloped implementation and governance pathway, and persistent equity gaps in low- and middle-income settings. Against this backdrop, two near- and medium-term translational pathways stand out: AI as a digital copilot for clinical evaluation and AI-enabled clinical trial design. Together, these approaches offer measurable workflow and trial-enabling benefits, with the potential to improve patient outcomes.