Ollama-driven medical insights using LLMs with a federated learning approach

Abstract

Traditional medical diagnostics often suffer from delays and inconsistencies due to the manual interpretation of unstructured patient data. To overcome these challenges, we introduce Our Model (given name as ‘AI Doctor’)—a novel diagnostic system built on the Ollama platform that integrates multiple pretrained large language models (Meditron, MedLLaMA2, WizardLM2, and Mistral) through an innovative prompt filtering mechanism. AI Doctor accurately interprets patient-reported symptoms to deliver precise diagnoses and personalized treatment recommendations, while its design supports robust local deployment and includes a theoretical framework for federated learning. This federated approach facilitates decentralized, privacy-preserving model updates across healthcare institutions. Performance evaluations using BLEU scores, structured output analysis, and inference speed measurements demonstrate that AI Doctor consistently outperforms individual models, ensuring high diagnostic accuracy and realtime clinical applicability.