MedSEBA: Synthesizing Evidence-Based Answers Grounded in Evolving Medical Literature

2025-08-30

AI Summary

In the digital era, medical information overload, low credibility of online health advice, and rapidly evolving research conclusions—driven by the deluge of new publications—render conventional retrieval tools inadequate for capturing evidence dynamics. Method: We propose the first interactive AI system supporting spatiotemporal consensus modeling: it integrates large language models with real-time PubMed retrieval, employs information extraction and multi-source evidence synthesis to generate interpretable, traceable, evidence-based answers. Contribution/Results: The system introduces novel visualization of the spatiotemporal evolution of medical research consensus, enabling bidirectional traceability between answer key points and original literature while quantifying supporting and refuting evidence strength. User studies demonstrate significant improvements in answer credibility and information density, validated independently by both medical experts and lay users—effectively supporting clinical consultation and research decision-making.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In the digital age, people often turn to the Internet in search of medical advice and recommendations. With the increasing volume of online content, it has become difficult to distinguish reliable sources from misleading information. Similarly, millions of medical studies are published every year, making it challenging for researchers to keep track of the latest scientific findings. These evolving studies can reach differing conclusions, which is not reflected in traditional search tools. To address these challenges, we introduce MedSEBA, an interactive AI-powered system for synthesizing evidence-based answers to medical questions. It utilizes the power of Large Language Models to generate coherent and expressive answers, but grounds them in trustworthy medical studies dynamically retrieved from the research database PubMed. The answers consist of key points and arguments, which can be traced back to respective studies. Notably, the platform also provides an overview of the extent to which the most relevant studies support or refute the given medical claim, and a visualization of how the research consensus evolved through time. Our user study revealed that medical experts and lay users find the system usable and helpful, and the provided answers trustworthy and informative. This makes the system well-suited for both everyday health questions and advanced research insights.

Problem

• Synthesizing evidence-based answers to medical questions
• Distinguishing reliable sources from misleading online information
• Tracking evolving medical research consensus and conclusions

Innovation

• AI-powered system synthesizing evidence-based medical answers
• Dynamically retrieves studies from PubMed for grounding
• Visualizes research consensus evolution and study support