東北大学大学院医学系研究科・医学部

STAFF

Professor

• Yamamoto YoichiroProfessor. 山本　陽一朗 教授 (兼任)
  

CONTACT

OUTLINE

Our ultimate goal is to discover novel disease mechanisms, to find new therapies or to choose the best treatment for each patient through the combination of the state-of-the-art AI technology and medical big data including multiscale information, which provides the link between molecular biology and clinical medicine. We establish a comprehensive analysis of medical information through collaboration with clinical doctors, which will contribute to the cure of currently incurable patients.

医療画像や臨床データ等を含む多階層の医療ビッグデータに対して、最新の人工知能と数理解析、医学分野における先人の叡智を結び付けた解析を行っています。未だ明らかになっていない疾患メカニズムの解明や、新規治療法の発見、また患者さん毎に最適な治療方法を選択するシステムの開発を研究目的とし、臨床の現場と協力しながら、医学データの統合的解析を進めることで、患者さんの治療に役に立つ研究を進めていきます。

• 

  理化学研究所革新知能統合研究(AIP)センター（日本橋、東京）にて。
  At RIKEN Center for Advanced Intelligence Project (AIP), Tokyo.

• 

  前立腺病理標本の連続切片に対するAI解析
  AI analysis on serial sections of prostate pathology specimens.

ARTICLE

Akatsuka J, et.al. Clinically informed intermediate reasoning enables generalizable prostate cancer prognostication through machine learning in limited settings. npj Digital Medicine, 9: 19, 2026.
URL：https://www.nature.com/articles/s41746-025-02193-x

Noguchi A, et.al. Deep learning predicts 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology. Scientific Reports 14:17059, 2024.
URL：https://www.nature.com/articles/s41598-024-67757-5

Yamamoto Y, et.al. Automated acquisition of explainable knowledge from unannotated histopathology images. Nature Communications 10(1):5642-5642, 2019.
URL：https://www.nature.com/articles/s41467-019-13647-8

Yamamoto Y, et.al. Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach. Scientific Reports 7:46732, 2017.
URL：https://www.nature.com/articles/srep46732

Yamamoto Y, et.al. Tumour and immune cell dynamics explain the PSA bounce after prostate cancer brachytherapy. British Journal of Cancer 115(2):195-202, 2016.
URL：https://www.nature.com/articles/bjc2016171

LINK

• がんAI予測の「二重の壁」を超える －施設差や検体差に左右されない医療の実現へ－
• 人工知能(AI)によって膵臓がん患者腹水中の生存関連因子を見つける ―腹腔内の免疫細胞が膵臓がん患者の生存期間に影響―