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Advanced AI Medicine Lab
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A lab that uses AI and medical data to aim at predicting disease and enabling personalized medicine.
AboutAbout the lab
Reading medicine’s “not yet visible future” with data and AI
The lab uses artificial intelligence and data science to tackle problems in medicine and healthcare.
It aims to draw out new ways of seeing disease by linking diverse medical data, including electronic health records, health checkups, medical images, genetic and omics data, and wearable data.
It pursues predicting disease onset and severity, understanding differences between patients, and understanding disease mechanisms at the molecular level as one continuous flow.
The lab aims to use AI not merely as a tool but as a foundational technology that changes how medicine and medical research are done.
Key terms
- Stratification
- Dividing patients into groups with similar disease characteristics. It reveals differences within the same diagnosis.
- Omics
- A collective term for data that comprehensively examine genes and their functions, such as genomics and transcriptomics
- Tensor decomposition
- A mathematical method that breaks large data with multiple axes into several components to extract patterns such as changes over time
ResearchResearch themes
The lab uses AI and mathematical sciences to study how to read disease onset and progression to severe illness from medical data. It pursues “predicting,” “finding differences,” and “understanding mechanisms” as one integrated flow, aiming to create new forms of medicine.
*Illustrative imagePredicting disease onset and severity
The lab develops methods to find “signs” hidden in complex time-series data. Examples include an analysis that captures the risk of nephrotic syndrome onset from combinations of multiple past medical histories, and research that extracts time-dependent changes in vaccine adverse reactions using tensor decomposition.
*Illustrative imageStratification to capture differences between patients
This research uses AI to capture differences between patients and understand disease in finer detail. In ovarian cancer research, patients were classified from blood test data, showing that groups with different characteristics exist even within the same early-stage cancer. The lab also works on models that stably predict diabetes subtypes.
*Illustrative imageUnderstanding disease at the molecular level
The lab analyzes omics data and molecular networks to understand results at the molecular level. Through transcriptional regulatory network analysis that integrates large-scale ChIP-seq data, a method has been developed that estimates transcription factor activity from gene expression with high accuracy. The lab is also advancing analyses of skin diseases.
MembersFaculty
- ProfessorEiryo Kawakami
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: “CSIRO ScienceImage 2465 Sequencing DNA” Health Sciences and Nutrition, CSIRO / CC BY 3.0 (cropped)
- Predicting disease onset and severity: “Kidney Glomerulus Cell Types” Artwork by Holly Fischer / CC BY 3.0 (cropped)
- Stratification to capture differences between patients: “2026 - Blood test samples” Oleg Yunakov / CC BY-SA 4.0 (cropped)
- Understanding disease at the molecular level: “Omics introductory overview” Jsagesid / CC BY 4.0 (cropped)
- Related lab (Systems Metabolism Lab): “Mass Spectrometry Laboratory ( )” IAEA Imagebank / CC BY-SA 2.0 (cropped)
- Related lab (Geriatric and General Internal Medicine Lab): “Nurse in geriatry” I Craig from Glasgow, Scotland / CC BY 2.0 (cropped)
- Related lab (Cancer Genome Informatics Lab): “ABI 3100 16-capillary DNA sequencer - Showing the capillary array and blocks and syringes” Michael Pereckas / CC BY-SA 2.0 (cropped)
- Related lab (Molecular Neuroscience Lab): “Neuron in the microchannel of hydrogel implant” Wisstock / CC BY 4.0 (cropped)
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