Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation
On the map: Retrieval-augmented generation → Medical and biomedical RAG
Abstract (arXiv)
The causal relationships between biomarkers are essential for disease diagnosis and medical treatment planning. One notable application is Alzheimer's disease (AD) diagnosis, where certain biomarkers may influence the presence of others, enabling early detection, precise disease staging, targeted treatments, and improved monitoring of disease progression. However, understanding these causal relationships is complex and requires extensive research. Constructing a comprehensive causal network of biomarkers demands significant effort from human experts, who must analyze a vast number of research papers, and have bias in understanding diseases' biomarkers and their relation. This raises an important question: Can advanced large language models (LLMs), such as those utilizing retrieval-augmented generation (RAG), assist in building causal networks of biomarkers for further medical analysis? To explore this, we collected 200 AD-related research papers published over the past 25 years and then integrated scientific literature with RAG to extract AD biomarkers and generate causal relations among them. Given the high-risk nature of the medical diagnosis, we applied uncertainty estimation to assess the reliability of the generated causal edges and examined the faithfulness and scientificness of LLM reasoning using both automatic and human evaluation. We find that RAG enhances the ability of LLMs to generate more accurate causal networks from scientific papers. However, the overall performance of LLMs in identifying causal relations of AD biomarkers is still limited. We hope this study will inspire further foundational research on AI-driven analysis of AD biomarkers causal network discovery.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 34 of its 52 references that are in the map. First posted on arXiv 1 Apr 2025.
Builds on
34 of 52 references.
Closest ancestors · 3
Same direction, newest first
- 2024Rationale-Guided Retrieval Augmented Generation for Medical Question Answering1 Nov
- 2024BioRAG: A RAG-LLM Framework for Biological Question Reasoning2 Aug
- 2024Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models27 Jan
Other references · 31
- 2017Attention Is All You Needcited by 60,361
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
- 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 800
- 2023Enabling Large Language Models to Generate Text with Citationscited by 595
- 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
- 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
19 more
- 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
- 2023Can Large Language Models Infer Causation from Correlation?cited by 146
- 2018Analyzing Uncertainty in Neural Machine Translationcited by 126
- 2023Benchmarking large language models for biomedical natural language processing applications and recommendationscited by 78
- 2023Can large language models build causal graphs?cited by 73
- 2023Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluationcited by 69
- 2023Self-Knowledge Guided Retrieval Augmentation for Large Language Modelscited by 69
- 2022LMPriors: Pre-Trained Language Models as Task-Specific Priorscited by 56
- 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 53
- 2024RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedbackcited by 46
- 2022Retrieval-based Controllable Molecule Generationcited by 44
- 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preferencecited by 38
- 2023Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesiscited by 25
- 2017Fast Causal Inference with Non-Random Missingness by Test-Wise Deletioncited by 23
- 2023Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Taskscited by 21
- 2024Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Modelscited by 16
- 2024Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generationcited by 15
- 2024Addressing Uncertainty in LLMs to Enhance Reliability in Generative AIcited by 15
- 2025Large Language Models in Bioinformatics: A Surveycited by 6
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Causal-Enhanced AI Agents for Medical Research Screening
- 2025Cross-modal Causal Intervention for Alzheimer's Disease Prediction
- 2025AD-GPT: Large Language Models in Alzheimer's Disease
- 2025BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring
- 2026Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis
- 2024Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models
- 2026Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
- 2025Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs