mamabench and mamaretrieval: Benchmarks for Evaluating Medical Retrieval-Augmented Generation in Maternal, Neonatal, and Reproductive Health
arXiv:2606.29467v1 Announce Type: new Abstract: Medical question-answering benchmarks rarely cover the maternal, neonatal, child, and reproductive-health questions a nurse-midwife asks, and, to our knowledge, no public chunk-level relevance benchmark exists for maternal-health guideline retrieval. We release two benchmarks that fill these gaps. mamabench is a scope-filtered QA set of 25,949 items assembled from seven existing expert-authored sources across multiple-choice, short-answer, and rubric-graded tracks; to help users calibrate the LLM judge that scores the rubric track, we re-scope He
The proliferation of Large Language Models (LLMs) and the demand for reliable, domain-specific AI applications necessitate robust, specialized benchmarks for evaluation, especially in critical fields like medicine.
This development addresses a significant gap in medical AI evaluation, enhancing the reliability and safety of retrieval-augmented generation (RAG) systems for maternal, neonatal, and reproductive health, which impacts patient outcomes and trust in AI.
The availability of 'mamabench' and 'mamaretrieval' provides standardized tools for developers to improve the accuracy and relevance of medical RAG systems, potentially leading to more effective and trustworthy AI assistants in healthcare.
- · AI healthcare developers
- · Medical AI researchers
- · Patients in maternal and neonatal care
- · Clinical decision support systems
- · General-purpose medical QA systems without specialized benchmarks
- · AI models that cannot perform well on nuanced medical data
Improved performance and decreased hallucinations in medical RAG applications focusing on maternal, neonatal, and reproductive health.
Increased adoption of AI tools by healthcare professionals as trust in their accuracy and relevance grows.
Potential for new regulatory standards for medical AI based on benchmark performance and demonstrable safety in specialized domains.
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