All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing
This addresses data scarcity and curation challenges for AI firms, biopharma, and medical device makers in medical imaging, though it is incremental as it builds on existing data pipeline methods.
The paper tackles data imbalance and high costs in medical imaging AI R&D by establishing a commercial platform that collects, selects, annotates, and pre-processes data, focusing on underrepresented Asian sources like Japan, and provides ready-to-use datasets to companies.
Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI. DataHub Website: https://medical-datahub.ai/