Quantitative Evidence Mining for Plausibility-Aware Biomedical AI: A Narrative Review and Conceptual Framework

2026-09-26T17:51:55.454Z

• title: Quantitative Evidence Mining for Plausibility-Aware Biomedical AI → Quantitative Evidence Mining for Plausibility-Aware Biomedical AI: A Narrative Review and Conceptual Framework
• summary: Biomedical artificial intelligence (AI) systems increasingly extract, organize, and reuse scientific claims from literature, clinical trials, and regulatory documents. But automatic extraction alone does not make a claim reliable evidence: a claim becomes useful only when it can be traced to its source, linked to the quantitative details that support it, and read within its biomedical context and uncertainty. This matters as large language models (LLMs) and increasingly autonomous systems drive evidence synthesis, knowledge graph (KG) construction, and decision support. Many text-mining and LL → Biomedical artificial intelligence is moving from literature retrieval toward evidence synthesis for knowledge graphs, clinical decision support, and computational models. Yet most information-extraction systems still represent findings as simple relations, discarding the quantitative and contextual detail needed for interpretation and reuse. A claim that one entity affects another is insufficient when the magnitude, unit, population, comparator, experimental conditions, uncertainty, and provenance are missing. We define quantitative evidence mining as a framework for transforming biomedical f