Doctor Saheb

Full-stack healthcare platform featuring LLM-assisted symptom guidance, Spring Security JWT auth, MongoDB, and Caffeine caching.

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Context / Event

Healthcare Engineering Project

The Problem & Real-World Friction

Patients needing quick health guidance often face delayed triage and lack secure, authenticated platforms to track consultations.

02. The Engineered Solution

Architectural & Technical Approach

Built a full-stack platform featuring LLM-assisted symptom guidance with Groq AI, secured via Spring Security and JWT authentication, backed by MongoDB and Caffeine in-memory caching for low latency.

03. Core Capabilities Breakdown

Key Technical Highlights

AI Healthcare Guidance & Spring Security System

Sub-Second Groq AI Inference

High-speed symptom guidance engine powered by Groq Llama inference providing fast initial medical recommendations and triage.

Stateless Spring Security & JWT

Role-based access control protecting patient consultation history and doctor appointments with cryptographically signed JWT tokens.

Multi-Layer Caffeine Caching

In-memory Caffeine cache layer caching high-frequency symptom databases to ensure sub-10ms response times for common queries.

MongoDB Document Persistence

Flexible NoSQL schema accommodating dynamic consultation notes, prescription structures, and polymorphic medical history logs.

04. Measured Outcome & Impact

Project Results & Impact

Delivered secure, cached medical symptom triage with sub-second API response times.

Technologies Used

8 tools

JavaSpring BootSpring SecurityReactMongoDBGroq & LlamaJWT AuthenticationCaffeine Cache

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