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Multimodal synthesis using explainable AI aims to reduce unnecessary surgeries and personalize treatment, while addressing implementation hurdles including data heterogeneity, hardware needs, and regulatory 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Hyun Park 1, Younghyun Park 2, Yong Moon Lee 3, Sejung Yang 2, * and Jong Ho Yoon 1, *  \nAcademic Editor: Dania Cioni  \nReceived: 25 February 2026  \nRevised: 29 March 2026  \nAccepted: 31 March 2026  \nPublished: 3 April 2026  \nCopyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 Department of Surgery, Yonsei University Wonju College of Medicine, Wonju 26492, Republic of Korea; [jhoney@yonsei.ac.kr](jhoney@yonsei.ac.kr)  \n2 Department of Medical Informatics and Biostatistics, Yonsei University Wonju College of Medicine, Wonju 26492, Republic of Korea; [pyh2908@yonsei.ac.kr](pyh2908@yonsei.ac.kr)  \n3 Department of Pathology, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; [12200301@dankook.ac.kr](12200301@dankook.ac.kr)  \n* Correspondence: [syang@yonsei.ac.kr](syang@yonsei.ac.kr) (S.Y.); [gsyoon@yonsei.ac.kr](gsyoon@yonsei.ac.kr) (J.H.Y.)  \nSimple Summary  \nThis 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 hospitalsand 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.  \nAbstract  \nBackground: 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. Objective: 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—largescale, multimodal architectures pre-trained on diverse datasets—to bridge the gap between initial pixels and long-term prognostic prediction. Proposed Models: 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. Key Innovations: 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. Challenges and Outlook: 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.  \nKeywords: thyroid cancer; personalized treatment; artificial intelligence (AI); foundation model  \n1. Introduction  \nThyroid cancer presents a significant and growing global health challenge. With incidence rates having tripled over the past three decades and mortality rates remaining stubbornly high despite therapeutic advancements, it is currently ranked as the fifth most diagnosed cancer among women aged 15–49 worldwide [1–3] . Despite sophisticated detection technologies, particularly advancements in imagi","cbCaiiCbJZhToiEa","https://ap.wps.com/l/cbCaiiCbJZhToiEa","pdf",708280,18,"English","# Introduction\n## Thyroid cancer as a growing health challenge\n## Limitations of traditional diagnostics\n## Emerging role of AI technologies","[{\"question\":\"What problem does the review target in thyroid cancer diagnosis and care?\",\"answer\":\"The review targets rising thyroid cancer incidence paired with diagnostic uncertainty from ultrasound-guided fine-needle aspiration, including inter-observer variability and indeterminate results.\"},{\"question\":\"What integrated AI foundation model frameworks are proposed?\",\"answer\":\"It proposes two frameworks: ThyroSight-Prognos for high-precision assessment in specialized tertiary settings and SonoPredict-AI for cost-effective screening in primary care.\"},{\"question\":\"How do the models aim to improve clinical decisions?\",\"answer\":\"By synthesizing multimodal data—ultrasound, pathology whole-slide imaging, genomics, and clinical parameters—using explainable AI tools to reduce unnecessary surgeries and personalize treatment pathways.\"}]","From Pixels to Prediction: Developing Integrated AI Foundation Models for Personalized Thyroid Cancer Care | PDF",1790137153,45]