Bheeshma Diagnosis: Megallm-Powered AI Medical Assistant Scales to 20,000+ Records

This article examines the performance characteristics of Bheeshma Diagnosis, an AI medical assistant built with Python and trained on a 20,000-record dataset. It explores how the system leverages megallm principles to balance accuracy and latency.

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Bheeshma Diagnosis is an AI medical assistant built with Python and trained on a 20,000-record dataset spanning symptoms, conditions, diagnostic pathways, and treatment recommendations. The system aims to balance accuracy and latency, as medical AI assistants cannot afford to be slow but also cannot sacrifice diagnostic precision. This is achieved through intelligent data preprocessing and optimized retrieval pipelines, which reduce the computational burden on the generative language model. The megallm paradigm - building systems that maximize the utility of large language models through smart orchestration - is central to Bheeshma Diagnosis' architecture. Key performance metrics include response latency, accuracy at scale, memory footprint, and throughput under concurrent load. The article offers lessons for developers, including the importance of curated datasets, preprocessing, and the megallm approach of orchestrating smaller, specialized components around a language model.