Health, Medical Care, and Welfare
We are developing diagnosis and drug development technologies so that people can enjoy long, healthy lives.
Leading-edge AI technology will change cancer diagnosis and drug discovery
Society is requiring the technology not only for medical treatment but also for preventive healthcare in order that people can live a healthy and long life. However, it is becoming the serious social problem that the heavy burden is placed on the doctors and paramedics with knowledge and advanced abilities.
To overcome this situation, we are actively working on the research of the medical engineering that will support doctors and improve diagnostic accuracy by using technology such as image processing and machine learning. We also challenging to develop the integrated support system for cancer diagnosis, e.g. the computer-aided detection system for breast ultrasonography.
In addition, through the development of new statistical methods with efficient algorithms, combinatorial factors causing genetic complex diseases become uncovered. Through collaboration with medical organizations, we have entered a stage of verifying the effectiveness of proposed technologies.
In addition, we are also focusing on supporting drug discovery that combines robot experiments and AI. Through AI and robot driven bio research, automated searches of diagnostic markers and lower cost individualized healthcare will become possible. This effort has been recognized globally, and in the Multimer Predictions category of the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP 2016), AIRC was honored by being awarded 1st place worldwide.
To overcome this situation, we are actively working on the research of the medical engineering that will support doctors and improve diagnostic accuracy by using technology such as image processing and machine learning. We also challenging to develop the integrated support system for cancer diagnosis, e.g. the computer-aided detection system for breast ultrasonography.
In addition, through the development of new statistical methods with efficient algorithms, combinatorial factors causing genetic complex diseases become uncovered. Through collaboration with medical organizations, we have entered a stage of verifying the effectiveness of proposed technologies.
In addition, we are also focusing on supporting drug discovery that combines robot experiments and AI. Through AI and robot driven bio research, automated searches of diagnostic markers and lower cost individualized healthcare will become possible. This effort has been recognized globally, and in the Multimer Predictions category of the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP 2016), AIRC was honored by being awarded 1st place worldwide.
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Theme1Computer-aided diagnosis in medical imaging based on machine learning
The advances in image recognition technology enable the machine learning system to support diagnosis in medical imaging. This technology is expected to contribute the great reduction of the burden on doctors and the improvement of diagnostic accuracy when it is used for screening and double checking. Specifically, a support system is currently under development that analyzes breast ultrasonography data and histopathological images to automatically detects suspected lesion . -
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Theme2AI application to causal factor detection in genetic disorders
We are developing new techniques to understand the causal factors complex genetic disorders that to date have not been understood. Through the development of new statistical techniques with efficient algorithms, it will become possible to uncover factors caused from simultaneous genetic mutations. We will partner with medical organizations and conduct verification testing. -
Future prospects
- Supporting technology for diagnosis and decision of course of treatment, based not only on genetic information but also on examination information and lifestyle information
- Drug discovery support that combines robot experimentation and AI