%0 Conference Proceedings
%T Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems
%+ Graduate School of Information Science and Technology [Tokyo]
%+ University of Cambridge [UK] (CAM)
%+ National Institute of Informatics (NII)
%+ Keio University School of Medicine [Tokyo, Japan]
%A Han, Changhee
%A Rundo, Leonardo
%A Murao, Kohei
%A Nemoto, Takafumi
%A Nakayama, Hideki
%Z Part 6: Medical-Health Systems
%< avec comité de lecture
%@ 978-3-030-49185-7
%( IFIP Advances in Information and Communication Technology
%B 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Neos Marmaras, Greece
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-584
%N Part II
%P 320-333
%8 2020-06-05
%D 2020
%R 10.1007/978-3-030-49186-4_27
%K Translational research;Computer-aided diagnosis;Generative adversarial networks;Data augmentation;Physician training
%Z Computer Science [cs]Conference papers
%X Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs’ clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-04060679/document
%2 https://inria.hal.science/hal-04060679/file/500087_1_En_27_Chapter.pdf
%L hal-04060679
%U https://inria.hal.science/hal-04060679
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-584