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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

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Published on: June 9, 2023

Artificial Intelligence Applications in Thyroid Cancer Care.

Nikita Pozdeyev^1,2,3,4, Samantha L White¹, Caitlin C Bell²

¹Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO 80045, USA.

The Journal of Clinical Endocrinology and Metabolism

|September 25, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) offers significant potential to enhance thyroid cancer diagnosis and management. However, widespread clinical adoption requires more high-quality, independent validation of AI tools in trials.

Keywords:

artificial intelligence deep learning digital pathology large language models thyroid cancer thyroid ultrasound

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Area of Science:

Medical Informatics
Oncology
Artificial Intelligence

Background:

Artificial intelligence (AI) presents numerous opportunities to advance thyroid cancer care.
AI applications are rapidly evolving in the diagnosis and management of thyroid cancer.

Purpose of the Study:

To review key applications of AI in thyroid cancer diagnosis and management.
To highlight impactful research and discuss the evolution of AI in thyroid cancer care.
To address current limitations and future directions for AI implementation in thyroid cancer.

Main Methods:

PubMed database search using the query "artificial intelligence thyroid cancer" until May 31, 2025.
Selection of high-impact publications based on technical innovation, dataset size, and validation rigor.
Review of AI applications including computer vision for ultrasound, metastasis detection, digital pathology, and large language models.

Main Results:

Computer vision for thyroid ultrasound nodule evaluation is a primary focus with significant clinical potential.
AI shows promise in detecting lymph node metastases, analyzing pathology slides, and processing unstructured data.
Current limitations include a lack of prospective validation, uncertain clinical utility, and implementation challenges.

Conclusions:

AI is poised to improve thyroid cancer diagnosis, lower costs, and enable personalized treatment.
High-quality, independent prospective validation is crucial for broad clinical adoption of AI in thyroid cancer.