Your AI product works great in demos and local testing. Then you put it into production only to see it fail spectacularly. What went wrong? How to spot scalability issues in your code at the early stages? Build your MVP with scalability in mind for your startup.
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In the medical field, clinicians often need to quickly access and understand patient case notes to make informed decisions. The sheer volume of data in these notes can be overwhelming, especially when time is critical. This is where Large Language Models (LLMs) can play a transformative role by generating summaries at varying levels of detail. In this blog post, we will explore how to define and implement these levels of abstraction to meet clinicians’ needs at different stages of their decision-making processes.
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AI for healthcare holds immense value for improving the patient outcomes. One key factor in this is the Clinical Decision Support Systems (CDSS). But how does one integrate the AI tools into complex healthcare workflows?
The CDSS adoption in healthcare happens through a series of progresses levels incrementally. Each level signifies an increase in the level of automation and decision-making support offered to clinicians. Each level builds upon the previous, enhancing capabilities and ensuring a safe transition from human-driven to AI-assisted and ultimately autonomous clinical support.
For AI enthusiasts, EHR system builders, and healthcare professionals, understanding these levels is crucial for developing, implementing, and leveraging CDSS effectively. Let’s look into what these levels are.
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Even with the advancements of Large Language Models (LLMs), there are still hurdles to overcome when it comes to applying the AI to an enterprise’s internal data.
However, there are several strategies enterprises can adopt to make LLMs work effectively to enable AI on their private data.
In this article, I will explain the concepts behind RAG, its limitations and how to overcome them, along with few real-world enterprise AI case-studies.
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