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

: Thyroid cancer incidence continues to rise globally, yet current diagnostic methods, reliant on ultrasound-guided fine-needle aspiration, suffer from substantial inter-observer variability and indeterminate results. : This review explores the transformative potential of integrated artificial intelligence (AI) foundation models in thyroid cancer management. We propose a paradigm shift using foundation models-large-scale, multimodal architectures pre-trained on diverse datasets-to bridge the gap between initial pixels and long-term prognostic prediction. : We introduce two integrated conceptual frameworks: ThyroSight-Prognos for high-precision assessment in specialized tertiary settings and SonoPredict-AI for cost-effective screening in primary care. : By synthesizing data from ultrasound, pathology (WSI), genomics, and clinical parameters through explainable AI (XAI), these models aim to reduce unnecessary surgeries and personalize treatment pathways. : This paper addresses critical implementation challenges, including data heterogeneity, hardware requirements, and regulatory trust, ultimately providing a strategic blueprint for future multi-center prospective clinical validation to revolutionize thyroid care through precision oncology.