ArticleJournal of the American Medical Informatics Association : JAMIA2024
RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Searching the druggable genome using large language models.Bioinformatics (Oxford, England) · 2026Article
- IAN, an intelligent system for omics data analysis and discovery.Cell reports methods · 2026Article
- Precision pharmacology: deep learning infused ontological framework with E-GRU enhancement for tailored medicine prescriptions.Scientific reports · 2026Article
- Searching the Druggable Genome using Large Language Models.bioRxiv : the preprint server for biology · 2026Article
- Article
- Structured taxonomy and framework for developing medical benchmark in large language models derived from scoping review.NPJ digital medicine · 2026Article
- Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Knowledge-Guided Explainable Recommendation Tool for Cancer Risk Prediction Models Using Retrieval-Augmented Large Language Models: Development and Validation Study.JMIR medical informatics · 2026Article
- AI-enhanced oncology MDT 2.0: from multi-modal data synergy to value-based care reconstruction - a systematic review of clinical efficacy and socioeconomic benefits.Frontiers in oncology · 2026Review
- Research on the development of an automated system for psychology questionnaire generation based on large language models.PloS one · 2026Article
- Bridging the computational-experimental gap: leveraging large language model to prioritize Alzheimer's therapeutics based on comparison of learning models.npj health systems · 2026Article
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
- Bridging the Computational-Experimental Gap: Leveraging Large Language Model to Prioritize Alzheimer's Therapeutics Based on Comparison of Learning Models.Research square · 2025Article
- AI misuse of retracted literature: A comparative study of ChatGPT4o, deepseek, and grok 3 in stem cell research.Die Naturwissenschaften · 2025Article
- Exploring multimodal large language models on transthoracic Echocardiogram (TTE) tasks for cardiovascular decision support.Journal of biomedical informatics · 2025Article
- Improving Large Language Model Applications in the Medical and Nursing Domains With Retrieval-Augmented Generation: Scoping Review.Journal of medical Internet research · 2025Article
- AI-Assisted Tools for Scientific Review Writing: Opportunities and Cautions.ACS applied materials & interfaces · 2025Review
- Improving automated deep phenotyping through large language models using retrieval-augmented generation.Genome medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
objectivesPrecise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND
methodsRefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison.
resultsThe overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration.
conclusionBy augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.