AutoMed: Multi-Agent AI System for Personalized Medical Knowledge Retrieval and Summarization

In today’s fast-paced medical environment, researchers and healthcare practitioners frequently find it difficult to keep pace with the sheer number of new articles and studies. Conventional search techniques, though exhaustive, tend to produce either too many irrelevant results or miss important information. To fix this, The research proposes a semi-autonomous, multi-agent and AI-driven solution that simplifies finding and synthesizing medical knowledge. By using agents to refine user queries, retrieve and filter articles from established databases like PubMed and PMC and generate summaries, the system makes searching more targeted. Breaking down complex queries makes it more specific, while intelligent filtering zooms in on the most relevant results. Summaries with source attribution are concise and trustworthy. Plus the system does user preference learning that sends users periodic updates on their areas of interest. Built-in explainability features adds to transparency and credibility so users can trust the curated outputs. Overall, this AI-driven solution is a flexible and time-saving tool for evidence-based decision making in clinical and research settings.