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

arXiv: https://arxiv.org/abs/2608.30393 · HTML: https://mlsanity.com/paper/2608.30393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 66 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 20 of 20)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
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- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2305.17100/) (2023)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://mlsanity.com/paper/2307.15343/) (2023)
- [PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge](https://mlsanity.com/paper/2401.11048/) (2024)
- [Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models](https://mlsanity.com/paper/2405.01686/) (2024)
- [A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences](https://mlsanity.com/paper/2412.03531/) (2024)
- [CQE: A Comprehensive Quantity Extractor](https://mlsanity.com/paper/2305.08853/) (2023)
- [Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data](https://mlsanity.com/paper/2104.01364/) (2021)
- [A Biomedical Knowledge Graph for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2302.04737/) (2023)
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- [In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform](https://mlsanity.com/paper/2005.02289/) (2020)
- [UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering](https://mlsanity.com/paper/2104.04549/) (2021)
- [What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval](https://mlsanity.com/paper/2106.14720/) (2021)
- [From Chaos to Clarity: Schema-Constrained AI for Auditable Biomedical Evidence Extraction from Full-Text PDFs](https://mlsanity.com/paper/2601.14267/) (2025)
- [Exploring Drug Safety Through Knowledge Graphs: Protein Kinase Inhibitors as a Case Study](https://mlsanity.com/paper/2603.00097/) (2026)
- [Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning](https://mlsanity.com/paper/2603.28325/) (2026)

## Abstract (arXiv)

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 findings into structured, context-rich, and auditable evidence units. We define the core elements of an evidence unit: the claim; measured entity and property; value, unit, or scale; comparator; population; biological or clinical conditions; temporal context; uncertainty; provenance; validation results; and expert-review status. We propose an eight-stage reference architecture spanning corpus selection, entity recognition, quantity extraction, context linking, normalization, evidence-unit assembly, multidimensional plausibility assessment, and export and governance. A central principle is that plausibility should not be collapsed into a single truth label; statistical, biological, methodological, contextual, and provenance-based support should remain explicit. The framework links information extraction to evidence synthesis and computational reuse, with applications in clinical-trial analysis, biomarker research, pharmacovigilance, knowledge-graph construction, and mechanistic modelling. It is a research agenda rather than a validated end-to-end system. Progress will require annotated multimodal benchmarks, rigorous component- and workflow-level evaluation, prospective testing, transparent provenance, and sustained expert oversight.