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

Jae Hyun Park1, Younghyun Park2, Yong Moon Lee3

  • 1Department of Surgery, Yonsei University Wonju College of Medicine, Wonju 26492, Republic of Korea.

Cancers
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) foundation models offer a new approach to thyroid cancer diagnosis, improving accuracy and reducing unnecessary surgeries. These advanced AI tools integrate diverse data for better patient outcomes.

Keywords:
artificial intelligence (AI)foundation modelpersonalized treatmentthyroid cancer

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Global thyroid cancer incidence is rising.
  • Current diagnostic methods like ultrasound-guided fine-needle aspiration have limitations, including variability and indeterminate results.

Purpose of the Study:

  • To explore the potential of integrated artificial intelligence (AI) foundation models in thyroid cancer management.
  • To propose a paradigm shift using foundation models for improved diagnostic and prognostic prediction.
  • To introduce conceptual frameworks for AI-driven thyroid cancer assessment.

Main Methods:

  • Utilizing large-scale, multimodal AI foundation models pre-trained on diverse datasets.
  • Synthesizing data from ultrasound, digital pathology (whole slide imaging), genomics, and clinical parameters.
  • Employing explainable AI (XAI) for transparent and trustworthy model outputs.

Main Results:

  • Proposed models (ThyroSight-Prognos and SonoPredict-AI) aim to enhance diagnostic precision and enable cost-effective screening.
  • AI integration is expected to reduce unnecessary surgeries and personalize treatment strategies.
  • Addressing challenges like data heterogeneity and regulatory trust is crucial for implementation.

Conclusions:

  • AI foundation models hold significant promise for revolutionizing thyroid cancer care through precision oncology.
  • Future multi-center prospective clinical validation is essential to confirm the efficacy of these AI frameworks.
  • The proposed approach aims to bridge the gap from initial imaging to long-term patient prognosis.