From the Bedside to the Digital World: Precision Medicine in Endocrinology with Al and ICT

Miyuki KataiJapan Speaker From the Bedside to the Digital World: Precision Medicine in Endocrinology with Al and ICTPrecision medicine in endocrinology must account for biological variability, life-course hormonal transitions, and sociocultural determinants of health. However, in routine clinical practice, endocrine disorders are often detected only after prolonged symptomatic periods, particularly when symptoms are nonspecific or overlap with normal physiological transitions. Our work originates from bedside clinical challenges. In developing and operating a comprehensive women’s specialty clinic grounded in sex-specific medicine—representing an innovative clinical model in Japan—we evaluated more than 5,000 women. Among patients who presented to our clinic with a prior diagnosis of menopausal disorders, organic diseases were identified in 27%. Thyroid dysfunction accounted for approximately 15% of cases initially attributed to menopausal disorders. These findings suggest that menopausal diagnoses may contribute to delayed recognition of underlying diseases. Among conditions masked by such symptoms, endocrine disorders were frequently identified, likely because many endocrine diseases require additional targeted laboratory testing for definitive diagnosis. Within endocrine disorders, thyroid dysfunction was particularly prevalent in women. To address this unmet need, we developed the Women’s AI Symptom Evaluator (WaiSE), a digital platform designed to visualize multidimensional symptom patterns using AI-assisted structured questionnaires. WaiSE was developed to support detection of a broad spectrum of underrecognized conditions in women, including endocrine disorders such as thyroid disease. Importantly, these digital tools help women recognize and articulate complex autonomic symptom patterns commonly experienced during menopausal transitions, thereby enabling clinicians to better interpret symptom presentations and facilitating earlier detection of endocrine disorders. The platform is supported by a gender-specific clinical database derived from over 5,000 patients and more than 60,000 consultations, enabling symptom–diagnosis correlation modeling and development of sex-informed diagnostic algorithms. Building upon this clinical and digital foundation, we have recently initiated an integrated endocrine screening strategy through collaboration with the AI-based Thyroid Screening (AITS) platform. We collaborated with Cosmic Corporation, the developer of the AI-based Thyroid Screening (AITS) system. AITS is an AI-based screening system that analyzes routine blood test results obtained in general screening programs, including health checkups, to estimate the likelihood of thyroid dysfunction. The integrated WaiSE–AITS system combines patient-reported symptom assessment through WaiSE with objective clinical indicators derived from AITS to assist in identifying individuals who may require additional thyroid function testing. The integrated system is being developed with the aim of future regulatory approval as Software as a Medical Device (SaMD). This integrated platform can be utilized in clinical practice settings as well as in health screening programs and occupational health settings, demonstrating feasibility in capturing real-world symptom data beyond hospital-centered care. The combined system is designed as a physician-supervised clinical decision-support tool intended to assist healthcare professionals in identifying patients who may benefit from further thyroid evaluation, while maintaining physician responsibility for final diagnostic decisions. This presentation highlights the clinical background, digital innovation process, and emerging collaborative screening strategies, demonstrating how bedside endocrinology can evolve into digitally supported precision care incorporating a life-course approach for women. Acknowledgements:This research was supported by AMED (Grant Number: JP21gk0210024h9903) and by grants from METI, Japan.