3S-1513
  • 日程10/17
  • 時間13:00-14:00
  • 場所105

This session has been closed

Precision Medicine: Introduction to Clinical Practice - Medical Application with AI -

While Japan already provides top level treatment in the medical field, new challenges have also begun for human and machines aiming for further improvement. By introducing AI to clinical practice, information that are determined by top special physicians can be provided to various medical doctors.

Recently, in the field of medicine, utilizing huge data sets and AI are gathering much expectation. By leveraging advanced information technologies, we can adequately extract medical knowledge and features from huge medical data sets, which leads to precision medicine and personalized medicine based on patient data.

How developed is the current advanced medical treatment? What about cancer precision medicine? How can evolving technology be utilized in clinical practice?
In this session, two specialists from the medical AI arena will speak around these topics from a cancer genome and pathology viewpoint.

  • Cancer Precision Medicine x AI : Chrovis by Xcoo
    In Japan, cancer precision medicine is gaining popularity as a cancer genetic panel test, which supports select drugs and clinical trials based on sequenced genomic data of tumors. Automatic and scalable data analysis with high reliability and high quality is a requirement. In addition, generation of personalized report based on updated medical knowledge and drug information is also important. In this session, Dr. Kunihiro Nishimura, developer of Chrovis, a solution for genomic medicine, will talk about his challenges in medical practice from an engineering viewpoint.

    • Speaker

      Xcoo, Inc.
      CEO / Founder

      Dr. Kunihiro Nishimura

  • Genomics x Pathology x Image Recognition AI: Luigi
    Although histopathological diagnosis is widely performed in medical institutions, it was difficult to compare images, or to make quantitative comparisons with other information, as histopathological images are not structured as genome data are. Dr. Shumpei Ishikawa's team discovered that deep texture information calculated from the neural network expresses histopathology well. This lead to their development of Luigi, a high-quality similar image search tool with this deep texture. As cancer genome mutation becomes inferable via histopathological images, it is no longer just the large medical facilities that can achieve accurate diagnosis, and to locate patient candidates who can benefit from cancer genomic medicine.

    • Speaker

      The University of Tokyo
      Graduate School of Medicine Professor

      Dr.Shumpei Ishikawa

CEATEC Executive Board
JEITA
Japan Electronics and
Information Technology Industries Association
CIAJ
Communications and
Information network Association
CSAJ
Computer Software Association of Japa