New Whitepaper: The Evolution of AI Systems ML vs. LLMs and Transforming Healthcare Through AI

From classic machine learning pipelines to the rise of large foundation models, healthcare is entering a new era of AI-driven transformation.

This white paper compares traditional ML systems with modern LLM-based approaches and explores what each means for healthcare regulation, deployment, and real-world use.

As the FDA begins to draw clear regulatory boundaries between conventional ML and generative AI, healthcare leaders must rethink how they build, monitor, and govern AI in clinical environments.

Authored by Assaf Mischari, Managing Partner at Team8, and Eyal Eliakim, Head of AI at Team8, this paper is designed to help healthcare leaders, investors, and operators navigate this shift, equipping them with frameworks, comparisons, and the critical questions needed to move forward with confidence.

In this paper, you’ll learn:

1. Why the FDA draws a clear line between traditional ML and GenAI in Healthcare: The agency has authorized few if any, LLM-powered medical devices, highlighting the need for new approval frameworks in high-stakes domains.
2. The core differences between ML and LLM pipelines: From data collection to deployment, see how these approaches differ in design, flexibility, and predictability.
3. The technical and operational trade-offs of using foundation models vs narrow purpose built models: Explore the challenges of deploying LLMs in sensitive sectors, including explainability, observability, and system cost.
4. How to begin evaluating and adapting AI responsibly: Get a preview of Team8’s framework for safely integrating LLMs into complex, regulated workflows.

Click here to read the full report and better understand the path forward for AI in healthcare: