Based on research I conducted in 2025, courtesy of a Kaggle hackathon, the dominant AI use case pursued in healthcare AI competitions hosted by Kaggle, is the use of medical images for diagnostics and prognostics. A natural progression of this research, was to analyze the radiology AI competitions, and the factors associated with successful clinical translation. I presented these findings at the Artificial Intelligence in Medicine and Imaging (AIMI) 2026 symposium.
This project explores why some use cases successfully translate into clinical practice while others remain confined to research only. A framework was developed based on the factors that influence real-world adoption, including clinical relevance, regulatory pathways, commercialization success factors, and workflow integration.
Radiology AI Commercialization Advisor
The Radiology AI Commercialization Advisor is an interactive framework, designed to evaluate factors associated with successful clinical translation.
Get answers to questions such as:
- Could my AI idea become a clinical product?
- What barriers might prevent adoption?
- Is there a viable FDA pathway?
- How does my idea compare with successful radiology AI solutions?
Connect
If you found the Radiology AI Commercialization Advisor™ useful, I would be grateful for your feedback, suggestions, or testimonial.
Please do not include confidential, proprietary, or patient-identifiable information.
You can also connect with me on LinkedIn at https://www.linkedin.com/in/gouri-prakash-0378b64/