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Medical AI can improve with new patient data, but every update must be checked first. Patients, equipment and ways of working change, and a model trained on older data can gradually get worse without anyone noticing.

Regulations in the U.S. and the EU state what must be done when medical AI is monitored and updated, but not in detail how. In the journal npj Digital Medicine, researchers at Karolinska Institutet present AEGIS, a framework for governing and monitoring medical AI models on the market.

AEGIS answers two questions: Can the new model be put into use? Does the model already in use need a closer look? The answers follow rules in a change plan approved before the device reached the market.

"Most AI-based medical devices are locked after approval and do not learn from new data. Every improvement needs retraining and validation," says Farhad Abtahi, head of the SMAILE core facility at the Department of Clinical Science, Intervention and Technology and corresponding author of the study.

The researchers demonstrated AEGIS with two models: one that identifies sepsis cases in past intensive care data and one that outlines brain tumors on MRI scans.

"The demonstrations examine the decision rules but do not show how safe or effective an AI model is in health care. Each clinical application requires its own validation and regulatory assessment," says Abtahi.

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