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A Learning Program for Treatment Recommendations by Molecular Tumor Boards and Artificial Intelligence
12
Zitationen
35
Autoren
2023
Jahr
Abstract
The findings of this quality improvement study suggest that use of a learning program improved the concordance of treatment recommendations provided by MTBs to central ones. Treatment recommendations made by an AI system showed higher concordance than that for MTBs, indicating the potential clinical utility of the AI system.
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Autoren
- Kuniko Sunami
- Yoichi Naito
- Yusuke Saigusa
- Toraji Amano
- Daisuke Ennishi
- Mitsuho Imai
- Hidenori Kage
- Masashi Kanai
- Hirotsugu Kenmotsu
- Keigo Komine
- Takafumi Koyama
- Takahiro Maeda
- Sachi Morita
- Daisuke Sakai
- Makoto Hirata
- Mamoru Ito
- Toshiyuki Kozuki
- Hiroyuki Sakashita
- Hidehito Horinouchi
- Yusuke Okuma
- Atsuo Takashima
- Toshio Kubo
- Shuichi Hironaka
- Yoshihiko Segawa
- Yoshihiro Yakushijin
- Hideaki Bando
- Akitaka Makiyama
- Tatsuya Suzuki
- Ichiro Kinoshita
- Shinji Kohsaka
- Yuichiro Ohe
- Chikashi Ishioka
- Kouji Yamamoto
- Katsuya Tsuchihara
- Takayuki Yoshino
Institutionen
- Tokyo National Hospital(JP)
- National Cancer Center Hospital East(JP)
- Yokohama City University(JP)
- Hokkaido University Hospital(JP)
- Okayama University Hospital(JP)
- Keio University(JP)
- The University of Tokyo(JP)
- Kyoto University(JP)
- Shizuoka Cancer Center(JP)
- Tohoku University Hospital(JP)
- Kyushu University(JP)
- Nagoya University Hospital(JP)
- Osaka Medical Center for Cancer and Cardiovascular Diseases(JP)
- Kyushu University Hospital(JP)
- Shikoku Cancer Center(JP)
- Yokosuka Kyosai Hospital(JP)
- Kyorin University Hospital(JP)
- Saitama Medical University(JP)
- Ehime University(JP)
- Gifu University Hospital(JP)