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Imai Lab (Medical Engineering and Informatics)
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At the intersection of medicine and informatics, the lab develops AI that supports diagnosis.
AboutAbout the lab
AI for healthcare, built on knowledge and language.
The Imai Lab studies the development and clinical application of medical artificial intelligence systems, in the interdisciplinary area where medicine, engineering, and informatics overlap.
It combines knowledge-based reasoning built on clinical knowledge databases with natural language processing using machine learning and large language models.
Its work includes computer-aided diagnosis and clinical care support, patient safety support, and knowledge discovery from large-scale clinical databases.
The lab also puts effort into social implementation, releasing what it builds as services that people actually use.
Key terms
- Large Language Model
- AI that has learned how words connect from large amounts of text. It is used to read and write text.
- Ontology
- A knowledge framework that organizes the meanings of things and the relationships between them in a form computers can process.
- Natural Language Processing
- Technology that has computers analyze text written by people and extract its meaning and structure.
ResearchResearch themes
The lab focuses on the interdisciplinary area where medicine, engineering, and informatics are closely intertwined, especially the development and clinical application of medical AI systems. In collaboration with the Medical Informatics division of the Graduate School of Medicine and the Department of Planning, Information and Management of the University Hospital, it aims to put real clinical data to use.
*Illustrative imageMedical Large Language Models
The lab builds large language models adapted for medicine and develops ways to apply them to systems that support diagnosis and clinical care. It has also worked on deep-learning-based analysis of electrocardiograms and on applying AI to mental health care.
*Illustrative imageClinical Medicine Ontology
The lab builds clinical medicine ontologies, which organize knowledge about diseases and bodily conditions in a form computers can process, and works on establishing their theoretical foundations.
*Illustrative imageDiscovering Knowledge from Clinical Text
Using natural language processing, the lab studies methods for automatically structuring the content of medical documents and discovering medical knowledge from them.
*Illustrative imageSupporting Diagnosis with Knowledge-Based Reasoning
The lab develops ways to apply knowledge-based reasoning methods built on clinical knowledge databases to diagnostic support systems. It has also worked on building diagnostic support systems for internal medicine.
MembersFaculty
- Associate ProfessorTakeshi Imai
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: “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Medical Large Language Models: “12 lead generated sinus rhythm” Glenlarson / Public domain (cropped)
- Clinical Medicine Ontology: “Social Network Analysis Visualization” Martin Grandjean / CC BY-SA 3.0 (cropped)
- Discovering Knowledge from Clinical Text: “Сховище Державного Архіву Одеської області” Костя Голобородов / CC BY-SA 4.0 (cropped)
- Supporting Diagnosis with Knowledge-Based Reasoning: “Black Stethoscope” ~riley / CC BY-SA 3.0 (cropped)
- Related lab (Medical Information Systems Lab): “EHR & EMR Software Development” Lunasora05 / CC BY 4.0 (cropped)
- Related lab (Ishikawa Lab): “Gastric adenocarcinoma” Nephron / CC BY-SA 3.0 (cropped)
- Related lab (Department of Microbiology (Takeda Lab)): “Human Respiratory Syncytial Virus (RSV)” NIAID / CC BY 2.0 (cropped)
- Related lab (Malaria Immunology Lab): “Anopheles stephensi” Jim Gathany / Public domain (cropped)
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