Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
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This work aims to identify/bridge the gap between Artificial Intelligence (AI) and Healthcare sides in Japan towards developing medical AI fitting into a clinical environment in five years. Moreover, we attempt to confirm the clinical relevance for diagnosis of our research-proven pathology-aware Generative Adversarial Network (GAN)-based medical image augmentation: a data wrangling and information conversion technique to address data paucity. We hold a clinically valuable AI-envisioning workshop among 2 Medical Imaging experts, 2 physicians, and 3 Healthcare/Informatics generalists. A qualitative/quantitative questionnaire survey for 3 project-related physicians and 6 project non-related radiologists evaluates the GAN projects in terms of Data Augmentation (DA) and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and its preliminary solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). The survey confirms our pathology-aware GANs' clinical relevance as a clinical decision support system and non-expert physician training tool. Radiologists generally have high expectations for AI-based diagnosis as a reliable second opinion and abnormal candidate detection, instead of replacing them. 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. We find that better DA and expert physician training would require atypical image generation via further GAN-based extrapolation.
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