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Open versus Closed AI Models in Healthcare

The podcast provides an overview : the critical trade-offs between using open-source and closed-source Artificial Intelligence (AI) models within healthcare settings. A central concern is the opacity of training data in many AI systems, which makes it challenging to anticipate biases or trust diagnostic outputs. The text highlights that open-source models promote clinical trust because researchers can inspect internal workings, facilitating robust safety science and the development of safety checks. Conversely, closed-source models are operationally easier to integrate into existing, secure clinical workflows that comply with regulations like HIPAA, making rapid deployment feasible. Ultimately, the piece concludes that both types of models are important, and the balance between transparency and ease of integration will influence future AI adoption and clinical training.