ReviewAnnual review of biomedical data science2025
The Development Landscape of Large Language Models for Biomedical Applications.
Review in Annual review of biomedical data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Responsible Use of Large Language Models in Microbial Genomics and Bioinformatics: A Life-Science Framework for Reliability, Reproducibility, and Risk-Aware Interpretation.Life (Basel, Switzerland) · 2026Review
- Memorization in large language models in medicine prevalence characteristics and implications.Nature communications · 2026Article
- Epistemic compression in large language model explanations of the gut-liver axis.Frontiers in cellular and infection microbiology · 2026Article
- Benchmarking Generative AI Tools for Interpretation of the WHO TB Mutation Catalogue.BMC digital health · 2026Article
- Commentary: GETgene-AI: a framework for prioritizing actionable cancer drug targets.Frontiers in systems biology · 2026Article
- Train-Time and Test-Time Computation in Large Language Models for Error Detection and Correction in Electronic Medical Records: A Retrospective Study.Diagnostics (Basel, Switzerland) · 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
15 authors.
Funding
Abstract
Large language models (LLMs) have become powerful tools for biomedical applications, offering potential to transform healthcare and medical research. Since the release of ChatGPT in 2022, there has been a surge in LLMs for diverse biomedical applications. This review examines the landscape of text-based biomedical LLM development, analyzing model characteristics (e.g., architecture), development processes (e.g., training strategy), and applications (e.g., chatbots). Following PRISMA guidelines, 82 articles were selected out of 5,512 articles since 2022 that met our rigorous criteria, including the requirement of using biomedical data when training LLMs. Findings highlight the predominant use of decoder-only architectures such as Llama 7B, prevalence of task-specific fine-tuning, and reliance on biomedical literature for training. Challenges persist in balancing data openness with privacy concerns and detailing model development, including computational resources used. Future efforts would benefit from multimodal integration, LLMs for specialized medical applications, and improved data sharing and model accessibility.
Indexed as
Identifiers
What Socratic 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 Socratic graph.