This successful project was the result of applying the AI technology expertise of Fujitsu Laboratories to the needs of the medical world. The Proof of Concept demonstrated that doctors could more than halve the time to access key research which can provide the validity of treatment plans for blood tumor.

Professor Seiya Imoto, Division of Health Medical Intelligence Human Genome Center The Institute of Medical Science, The University of Tokyo (IMSUT)
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The Institute of Medical Science, University of Tokyo

AI - halving the time to access key research


The Institute of Medical Science, The University of Tokyo is an affiliated institute specializing in medical research. Human Genome Center is a university research center established within IMSUT with a mission to promote human genome research and to practice genomic medicine. In the area of health and medical intelligence, research covers a wide range of fields, including human genome and symbiotic metagenomic data analysis, health and medical big data analysis and technology development, and the use of IoT devices.


To make effective use of analyzed genomic information, treatment plans currently require research across multiple sources of information on mutated genomes. It takes doctors a huge amount of time to sift through the research documentation and to determine specific treatment plans, even in situations where they use only some of the genomic information available.


AI technology is used to discover and extract literature relating to the specific genomic data required. Relevant text is highlighted to make it easy for doctors to use. Prognostic information is also selected from graphs and tables and linked to the relevant text.


  • The AI technology displays the key knowledge extracted from each paper, thereby reducing the time required to process the overall content
  • The AI technology filters the documentation to highlight relevant papers only, more than halving the time required to review each gene variation
  • The technology has the potential to overcome bottlenecks in the future when whole-genome information is used

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