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

arXiv: https://arxiv.org/abs/2509.00414 · HTML: https://mlsanity.com/paper/2509.00414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023)
- [BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers](https://mlsanity.com/paper/2404.18443/) (2024)
- [PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge](https://mlsanity.com/paper/2401.11048/) (2024)
- [Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval](https://mlsanity.com/paper/2404.08359/) (2024)
- [Comparing Knowledge Sources for Open-Domain Scientific Claim Verification](https://mlsanity.com/paper/2402.02844/) (2024)
- [On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2502.14759/) (2025)
- [NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing](https://mlsanity.com/paper/2406.15294/) (2024)

## Abstract (arXiv)

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.