A Randomized Controlled Trial to Evaluate an Artificial Intelligence-enabled Clinical Assistant Leveraging Large Language Models for Thyroid Cancer Staging and Risk Stratification Among Medical Students and Clinicians

With recent advancements in technology, AI has become widely applicable to visual text recognition in clinical settings. AI-powered text recognition is emerging as a highly efficient, sustainable, and cost-effective tool for decision making and personalised medicine. Numerous studies have employed natural language processing (NLP) algorithms, particularly large language models (LLMs), to convert unstructured free-text from clinical consultation notes within electronic health records (EHR) into structured data, thus enriching individual clinical profiles in the EHR databases. Over time, these AI models have continuously improved their predictive accuracy and performance through self-learning (or unsupervised learning). While AI models had made a significant impact in oncology practices overseas, their utility for text recognition in oncology remains limited in Hong Kong. This proposed study aims to evaluate the clinical feasibility of adopting AI-based models to improve time efficiency, accuracy, and end-users' confidence in diagnostic assessment and risk prediction, compared against traditional workflows without AI assistant for thyroid cancer management.