It’s time for a dynamic, system-based approach.
AI in healthcare is no longer just about models – it’s about complex, adaptive systems operating in dynamic clinical environments.
Yet, most deployments still follow an outdated, linear model:
training → deployment → monitoring → updates.
This approach ignores the fact that modern AI – especially large language models (LLMs) – should continuously learn from new data and user interactions. AI models are no longer static tools. They operate in changing contexts where interactions, data, interfaces, and users all influence outcomes.
In the near future, clinicians will interact with multiple AI systems simultaneously, and these models will collaborate with each other.
In such an environment, static deployment is no longer sufficient.
We need a dynamic approach to AI deployment, based on:
Continuous learning of models within the clinical environment.
A systems-level perspective that monitors the entire ecosystem (AI, user, interface, data).
Embedded feedback mechanisms enabling rapid response to performance degradation or shifts in patient populations.
This approach requires new standards of quality, infrastructure, and governance models.
But only through this shift can we truly close the implementation gap and make AI in healthcare not an elite technology, but an integral part of everyday care delivery.
The future of medicine is AI that evolves alongside patients, data, and clinicians.
