Miki, Yukio

A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians

Takita, H., Kabata, D., Walston, S. L., Tatekawa, H., Saito, K., Tsujimoto, Y., Miki, Y., & Ueda, D. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 203

Technical and clinical overview of deep learning in radiology

Ueda, D., Shimazaki, A., & Miki, Y. (2019) · Japanese Journal of Radiology

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 100

Evaluating GPT-4-based ChatGPT’s Clinical Potential on the NEJM Quiz

Ueda, D., Walston, S. L., Matsumoto, T., Deguchi, R., Tatekawa, H., & Miki, Y. (2023) · BMC Digital Health

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 45

Small artery dementia in Japan: radiological differences between CADASIL, leukoaraiosis and Binswanger’s disease

Tomimoto, H., Ohtani, R., Wakita, H., Lin, J.-X., Ihara, M., Miki, Y., Oshima, F., Murata, T., Ishibashi, K., Suenaga, T., et al. (2006) · Dementia and Geriatric Cognitive Disorders

Cerebrovascular and genetic disordersNeurological Disease Mechanisms and Treatments

Cited by 20

Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Matsumoto, T., Walston, S. L., Walston, M., Kabata, D., Miki, Y., Shiba, M., & Ueda, D. (2022) · Journal of Digital Imaging

COVID-19 diagnosis using AIMachine Learning in Healthcare

Cited by 15

Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study

Walston, S. L., Matsumoto, T., Miki, Y., & Ueda, D. (2022) · The British Journal of Radiology

COVID-19 diagnosis using AICOVID-19 Clinical Research Studies

Cited by 9