MedBioLM combines fine-tuning and retrieval-augmented generation to create a specialized language model that excels at medical and biological question answering.

• Optimizes both short-form and long-form medical queries
• Improves factual accuracy and reliability in specialized healthcare domains
• Integrates domain-specific knowledge retrieval to enhance contextual understanding
• Shows potential for supporting clinical decision-making and reasoning tasks

This research addresses a critical gap between general-purpose LLMs and specialized healthcare applications, potentially transforming how medical professionals access and utilize AI-powered information systems.

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation