# Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems

arXiv: https://arxiv.org/abs/2001.03923 · HTML: https://mlsanity.com/paper/2001.03923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [General-Purpose Multi-Modal OOD Detection Framework](https://mlsanity.com/paper/2307.13069/) (2023)
- [Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model](https://mlsanity.com/paper/2310.18355/) (2023)
- [All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing](https://mlsanity.com/paper/2403.06145/) (2024)

## Abstract (arXiv)

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.