The role of artificial intelligence in surgeon-performed ultrasonographic evaluation of cytologically indeterminate thyroid nodules

Abstract

<p>Introduction: Evaluating indeterminate thyroid nodules(ITN) is challenging, especially without molecular tests. This study examines whether artificial intelligence (AI) assistance can improve ITN diagnostic accuracy and bridge expertise gaps in surgeon-performed ultrasound. <br></p><p>Methods: 134 ultrasound clips from 67 patients with ITN were reviewed by doctors of four levels: endocrine-surgery specialist, senior residents, junior residents, and medical student. After a 2-week wash-out, they re-evaluated the clips using AI-SONIC, an AI platform analyzing ultrasound real-time to predict cancer risk. Performance was validated against final histopathology. <br></p><p>Results: Without AI, medical students, junior residents and senior residents performed significantly worse than specialists(AUROC 0.530–0.560 vs 0.771, p < 0.05). AI-SONIC improved residents' and medical students' diagnostic accuracy to levels comparable with specialists(AUROC 0.733–0.751 vs 0.771). The specialists’ performance remained unchanged with AI assistance. <br></p><p>Conclusion: AI enhances ultrasound evaluation of ITN by junior surgeons and medical students, elevating their accuracy to expert levels, supporting clinical assessment and medical education.<br></p>