From Pixels to Prediction: Developing Integrated AI Foundation Models for Personalized Thyroid Cancer Care

Simple Summary This study proposes using integrated AI foundation models to improve thyroid cancer diagnosis and treatment. Unlike previous narrow computer models, these advanced systems bridge the gap between initial medical imaging and long-term prediction by integrating multimodal data into specialized frameworks: ThyroSight-Prognos for specialized hospitals and SonoPredict-AI for cost-effective primary care. By making these models transparent through visual and clinical explainability tools (XAI), the study seeks to enhance trust and usability in hospitals. The potential impact is significant: reducing unnecessary surgeries and personalizing treatments while addressing technical feasibility, including specific hardware requirements and iterative clinician feedback cycles. For researchers, this work provides a strategic blueprint for future multi-center clinical validation, encouraging the integration of data-driven precision oncology into the global healthcare system.