As artificial intelligence becomes more embedded in healthcare, from diagnostics to clinical decision support to ambient listening, health systems must take a more rigorous, structured approach to testing and evaluating AI models locally before deploying them in clinical workflows. AI models aren’t plug-and-play. A model that performs well in a vendor's environment may underperform—or even introduce safety risks—once deployed at a specific healthcare site.
To enable the safe and effective deployment of AI in healthcare, local evaluation should focus on these four areas:
1.Software Quality.
2.Pressure Testing.
3.Fairness and Bias Mitigation.
4.Safety and Accuracy.
AI has the potential to support the next generation of care. However, without robust evaluation, it can amplify harm, introduce unseen biases, or create new patient risks. By establishing standardized approaches to local testing and demanding transparency from vendors, healthcare systems can confidently implement AI solutions while safeguarding patient well-being.