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Mathematical Intelligence Medicine Lab (RIKEN)
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This medical AI lab analyzes medical data across fields using AI and mathematics.
AboutAbout the lab
Integrating medical data with AI to generate new knowledge
The AI Medical Science lab analyzes multidimensional medical data, such as genomes, spatial omics, medical images, and clinical information, by combining AI, mathematics, and medical science.
The lab aims to clarify disease mechanisms, discover new therapies, and support the selection of treatments suited to each individual patient.
Research is carried out at two sites: the Seiryo Campus in Sendai, and RIKEN in Tokyo (the Mathematical Intelligence Medicine Course of the RIKEN Joint Graduate Program).
Professor Yoichiro Yamamoto also serves as a Team Director at the RIKEN Center for Advanced Intelligence Project.
Key terms
- Spatial omics
- A method that captures gene activity in individual cells together with their positions within tissue.
- Latent space
- An internal representation space of AI that summarizes the features of data in fewer dimensions.
- AI for Science
- An approach that uses AI as a tool for scientific research to advance the discovery of new findings.
NewsLab news
Newton special issue "Yoku Wakaru Jinko Chino no Subete: Saishinban" (an AI guide in Japanese, latest edition), supervised by Professor Yamamoto, has been published
The lab presented a new approach to cancer prediction that builds medical knowledge into the reasoning process of AI
Professor Yoichiro Yamamoto took up his post, and the AI Medical Science lab was established
ResearchResearch themes
In medical settings, data that differ in both dimensionality and nature, such as genomes, spatial omics, medical images, and clinical information, accumulate every day. A framework that integrates them across the board at the level of latent space and connects them to understanding disease and making treatment decisions has not yet been sufficiently established. The lab analyzes multidimensional medical data by combining AI, mathematics, and medical science, with the aim of creating new knowledge and applying it in medicine.
*Illustrative imageIntegrated analysis of multidimensional medical data
The lab analyzes data of different kinds, such as genomes, spatial omics, medical images, and clinical information, by combining AI, mathematics, and medical science. The aim is to create new knowledge and apply it in medicine.
*Illustrative imageAI consistent with medical knowledge
The lab explores AI that remains consistent with medical knowledge and accelerates scientific progress, even with limited data or imperfect observation conditions. This is an effort to find a new approach to "AI for Science."
MembersFaculty
The lab's members include faculty, visiting faculty, Moonshot researchers, doctoral and undergraduate students, graduate students who are working professionals and collaborative research members, and a secretary.As of 2026
- ProfessorYoichiro Yamamoto
- LecturerJunya Iwasaki
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “NOIRLab HQ Server Racks (6V6A0375-CC)” NOIRLab/NSF/AURA/T. Slovinský / CC BY 4.0 (cropped)
- Integrated analysis of multidimensional medical data: “Linkedin maps data visualization” Luc Legay / CC BY-SA 4.0 (cropped)
- AI consistent with medical knowledge: “AI relation to Generative Models subset, venn diagram” The Original Benny C / CC BY-SA 4.0 (cropped)
- Related lab (Artificial Intelligence in Medical Science Lab (Affiliated Center for Creative and Applied Medical Research)): “Graphics Processing Unit” Nick Stathas / CC BY-SA 4.0 (cropped)
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- Related lab (Hematology Lab): “Myeloid leukemia” Makysm / CC0 (cropped)
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