Healthcare organizations must choose between onsite or cloud-based validation for AI algorithms, considering accessibility and scalability.
Rigorous external validation infrastructures must prioritize security to safeguard patient information.
High-quality imaging data collection is essential, requiring alignment with real-world demographics.
Understanding computational requirements is crucial for deep-learning models to execute scoring processes effectively.
A comprehensive scoring protocol is necessary for reliable validation, including detailed documentation and systematic execution of inference processes.