Accelerating GenAI in Healthcare: Model Inference Caching at MedicLabs

 In my earlier post, Building a GenAI Medical Assistant/Advisor, I shared how generative AI can reshape patient interactions and medical decision support. While designing that system at MedicLabs, one challenge quickly became clear: raw AI power is impressive, but without speed and responsiveness, users lose trust.

The solution I implemented was Model Inference Caching — a methodology that transformed performance, reduced costs, and made the GenAI assistant practical for real-world healthcare.

Why Model Inference Caching Matters

Generative AI models are resource-intensive. Each inference — whether explaining lab results, summarizing patient records, or advising on treatment protocols — can consume significant compute power. Without optimization, response times lag, frustrating both patients and doctors.

Model inference caching solved this by:

• Reducing latency: Common queries (like routine lab interpretations) were cached, delivering instant answers.

• Lowering costs: Avoiding repeated inference runs saved GPU cycles and infrastructure expenses.

• Improving reliability: Cached outputs acted as a fallback when compute resources were under strain.

How I Applied Caching in MedicLabs

I designed caching to operate primarily on the admin side, ensuring that end-users always experienced top speed while administrators handled the heavier lifting.

Key strategies included:

• JSON Caching for AI outputs: Structured responses from the GenAI model were stored and reused when identical or similar queries appeared.

• Inference Embedding Caching: Embeddings for common medical terms and queries were cached, accelerating similarity searches.

• Hybrid Admin/User Strategy: Admin-side caching handled heavy lifting, while lightweight HTTP caching improved delivery of static assets.

This layered approach meant that doctors and patients interacting with the MedicLabs assistant received answers in seconds, not minutes.

Best Practices I Learned

Through implementation, I discovered several best practices for model inference caching:

• Cache intelligently, not blindly: Medical queries can be sensitive. I avoided caching patient-specific data, focusing instead on general medical knowledge and frequently repeated queries.

• Set expiration policies: Medical guidelines evolve. Cached outputs were given TTLs (time-to-live) to ensure accuracy.

• Balance freshness and speed: Inference caching worked best when paired with monitoring to detect outdated or stale responses.

When to Avoid Caching

Caching isn’t always appropriate. I avoided it in cases such as:

• Highly personalized queries (unique patient cases) that required fresh inference.

• Rapidly changing data (like real-time vitals) where accuracy was critical.

• Sensitive outputs (confidential patient information) that should never be cached.

Caching Meets GenAI

What excites me most is how caching and GenAI complement each other:

• Predictive Pre-Caching: AI models can anticipate likely queries and pre-cache them.

• Adaptive Caching: GenAI can decide dynamically whether to serve cached results or trigger fresh inference.

• Scalable Healthcare AI: With caching, GenAI assistants can serve thousands of patients simultaneously without bottlenecks.

Final Thoughts

Model inference caching was a cornerstone of making the MedicLabs GenAI Medical Assistant practical and scalable. It turned a powerful but resource-intensive system into a responsive, cost-efficient, and trustworthy tool for healthcare.

As I continue exploring AI in healthcare, I see caching not just as a performance hack, but as a strategic enabler of real-world GenAI adoption. By combining caching methodologies with intelligent inference design, we can build systems that are both smart and fast — exactly what modern healthcare demands.