Proactive Learning Strategies Boost Safety of Hospital AI Models, Study Finds
June 4, 2025
A York University-led study identifies that continual and transfer learning strategies can mitigate harmful data shifts in clinical AI models used in hospitals.
Key Details
- Data shifts between training and real-world hospital data can cause patient harm and model unreliability.
- Researchers analyzed 143,049 patient encounters from seven hospitals in Toronto using the GEMINI data network.
- Significant data shifts were observed between community and academic hospitals, with transfer of models from community to academic settings leading to more harm.
- Transfer learning and drift-triggered continual learning approaches improved model robustness and prevented performance drops, especially during the COVID-19 pandemic.
- A label-agnostic monitoring pipeline was proposed to detect and address harmful data shifts for safe, equitable AI deployment.
Why It Matters
Data distribution changes are common in real-world clinical environments, often leading to AI model bias or inaccuracy. This research provides practical, evidence-based strategies for continuously monitoring and adapting clinical AI models, helping ensure safer and more robust radiology-AI deployment in hospital settings.