Tech UPTechnologyEfficiency improvement in cancer genomic medicine goes through AI

Efficiency improvement in cancer genomic medicine goes through AI

Yes, it is good news, I think. It was announced by the Japanese company Fujitsu this week, together with the Institute of Medical Sciences of the University of Tokyo. Both have successfully verified how artificial intelligence (AI) improves the efficiency of treatment planning in genomic cancer medicine.

In this field, the creation of treatment plans derived from genomic information is still an expensive and time-consuming process, but with newly developed technology, a cancer genomic medicine knowledge graph can be generated, which can be used to create plans. treatment, including the effects of ongoing treatment.

Verification trial experiments using AI have enabled the Department of Hematology and Oncology at the University of Tokyo Institute of Medical Sciences to reduce the amount of work required to determine a treatment for acute myeloid leukemia by more than half, thus providing greater efficiency.

In the future, the Japanese company’s Laboratories will support the work of physicians, expanding artificial intelligence to address a greater variety of cancer types, and contribute to the general advancement of genomic cancer medicine.

Personalized medicine

The goal of genomic cancer medicine is to provide optimal medical care for each patient, by identifying genomic mutations in cancer patients and predicting the likelihood of disease, as well as the response to medications and side effects. As of June 2019 in Japan, cancer gene panel testing has been covered by health insurance and industry experts anticipate that the number of tests will increase.

Currently, in the field of genomic cancer medicine, it is still necessary for medical specialists to carefully search the relevant articles one by one from a database and determine the appropriate treatment methods, as well as their effects on the patient.

With artificial intelligence, a knowledge database is automatically generated on the relationship between genetic mutations and therapeutic medicines, and on the relationship between therapeutic medicines and their effects, from medical documents. This is achieved by integrating Fujitsu’s AI technology for language processing, which identifies the terms and phrases used in research papers from the context, as well as the information necessary to analyze the treatment policies identified by the Institute of Sciences. Doctors from the University of Tokyo.

With development done, 2.4 million item relationships from 860,000 medical documents are automatically extracted as knowledge, to build a graphics database for genomic cancer medicine.

This study measures the time required for four doctors specializing in hematological neoplasms from the Institute of Medical Sciences of the University of Tokyo to search and examine documents, using technology based on previous cases of acute myeloid leukemia, and the efficiency of the exam work with and without the new technology developed.

Thanks to artificial intelligence, the task of reading the entire document was reduced, by presenting the knowledge extracted from each one, and allowing users to focus only on the pertinent aspects of the investigation. As a result, it was confirmed that the amount of time spent on this task can be reduced by more than half, compared to the average of about 30 minutes for each study that took in the past. Currently, it is estimated that more than 12,000 people suffer from leukemia annually in Japan, and if genomic medical treatments are administered to all of them using this new technology, the 6,000 hours of examination work normally required for experts can be shortened. to 3,000 hours or less, which greatly speeds up the process of determining the appropriate treatment for each patient.

In the future, developments made with AI will be used to further improve the efficiency of the genomic mutation healing process. Fujitsu will further utilize the knowledge graph for precision medicine, developed through this joint research, to improve the efficiency of the study of genetic mutations for a wide range of cancer types and to actively promote the development of cancer genomics. in clinical practice.

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