Artificial intelligence and machine learning have the potential to greatly transform healthcare. We will begin with highlighting a few applications in radiology, oncology and ophthalmology. Although these techniques have shown remarkable performance for many tasks including medical image analysis, we will share some of the challenges that we have faced in developing robust and trustworthy algorithms including a lack of repeatability, explainability, generalizability, and the potential for bias. We will conclude with a some mitigating strategies and recommendations.

The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires  easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts over 50 TB of diverse publicly available cancer image data spanning radiology and microscopy domains. By harmonizing all  data based on industry standards and colocalizing it with analysis and exploration resources, IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and  transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected  by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and  cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. The presentation will discuss the recent developments in IDC, highlighting resources, demonstrations and examples that we hope can help you improve your everyday imaging research practices - both those that use public and internal datasets.