Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease

• Main Authors: Jae Hoon Moon, Steven R. Steinhubl
• Format: Article
• Language: English
• Published: Academya Publishing Co. 2019-06-01
• Series: Endocrinology and Metabolism
• Subjects: Thyroid; Thyroid neoplasms; Hyperthyroidism; Hypothyroidism; Artificial intelligence; Machine learning; Database; Wearable electronic devices
• Online Access: https://e-enm.org/Synapse/Data/PDFData/2008ENM/enm-34-124.pdf
• ISSN: 2093-596X; 2093-5978

Digital medicine has the capacity to affect all aspects of medicine, including disease prediction, prevention, diagnosis, treatment, and post-treatment management. In the field of thyroidology, researchers are also investigating potential applications of digital technology for the thyroid disease. Recent studies using artificial intelligence (AI)/machine learning (ML) have reported reasonable performance for the classification of thyroid nodules based on ultrasonographic (US) images. AI/ML-based methods have also shown good diagnostic accuracy for distinguishing between benign and malignant thyroid lesions based on cytopathologic findings. Assistance from AI/ML methods could overcome the limitations of conventional thyroid US and fine-needle aspiration cytology. A web-based database has been developed for thyroid cancer care. In addition to its role as a nationwide registry of thyroid cancer, it is expected to serve as a clinical platform to facilitate better thyroid cancer care and as a research platform providing comprehensive disease-specific big data. Evidence has been found that biosignal monitoring with wearable devices may predict thyroid dysfunction. This real-world thyroid function monitoring could aid in the management and early detection of thyroid dysfunction. In the thyroidology field, research involving the range of digital medicine technologies and their clinical applications is expected to be even more active in the future.