Projected Growth in FDA-Approved Artificial Intelligence Products Given Venture Capital Funding.

PURPOSE: Medical imaging accounts for 85% of digital health's venture capital funding. As funding grows, it is expected that artificial intelligence (AI) products will increase commensurately. The study's objective is to project the number of new AI products given the statistical association between historical funding and FDA-approved AI products. METHODS: The study used data from the ACR Data Science Institute and for the number of FDA-approved AI products (2008-2022) and data from Rock Health for AI funding (2013-2022). Employing a 6-year lag between funding and product approved, we used linear regression to estimate the association between new products approved in a certain year, based on the lagged funding (ie, product-year funding). Using this statistical relationship, we forecasted the number of new FDA-approved products. RESULTS: The results show that there are 11.33 (95% confidence interval: 7.03-15.64) new AI products for every $1 billion in funding assuming a 6-year lag between funding and product approval. In 2022 there were 69 new FDA-approved products associated with $4.8 billion in funding. In 2035, product-year funding is projected to reach $30.8 billion, resulting in 350 new products that year. CONCLUSIONS: FDA-approved AI products are expected to grow from 69 in 2022 to 350 in 2035 given the expected funding growth in the coming years. AI is likely to change the practice of diagnostic radiology as new products are developed and integrated into practice. As more AI products are integrated, it may incentivize increased investment for future AI products.

ACR's Connect and AI-LAB technical framework.

Objective: To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions. Materials and Methods: Among its core capabilities, ACR Connect provides educational resources; tools for dataset annotation; model building and evaluation; and an interface for collaboration and federated learning across institutions without the need to move data off hospital premises. Results: The AI-LAB application within ACR Connect allows users to investigate AI models using their own local data while maintaining data security. The software enables non-technical users to participate in the evaluation and training of AI models as part of a larger, collaborative network. Discussion: Advancements in AI have transformed automated quantitative analysis for medical imaging. Despite the significant progress in research, AI is currently underutilized in current clinical workflows. The success of AI model development depends critically on the synergy between physicians who can drive clinical direction, data scientists who can design effective algorithms, and the availability of high-quality datasets. ACR Connect and AI-LAB provide a way to perform external validation as well as collaborative, distributed training. Conclusion: In order to create a collaborative AI ecosystem across clinical and technical domains, the ACR developed a platform that enables non-technical users to participate in education and model development.