ArticleNature biomedical engineering2026
Making large language models reliable data science programming copilots for biomedical research.
Article in Nature biomedical engineering, 2026. 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.
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Performance, Failures, and Oversight of a Large Language Model Agent for Clinical Data Analysis: Evaluation Study.Journal of medical Internet research · 2026Article
- HealthFlow: automating electronic health record analysis via a strategically self-evolving multi-agent framework.NPJ digital medicine · 2026Article
- Empowering biomedical evidence exploration and synthesis with deep knowledge graph research.Nature machine intelligence · 2026Article
- Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models.PLoS computational biology · 2026Article
- SpikeLab: Agentic tools for spike data analysis.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) can generate impressive data visualizations from simple requests, yet their accuracy remains underexplored. Here we present a benchmark of 293 coding tasks derived from 39 studies across 7 biomedical research areas, including biomarkers, integrative analysis, genomic profiling, molecular characterization, therapeutic response, translational research and pan-cancer analysis. Benchmarking eight proprietary and eight open-source LLMs under various prompting strategies reveals an overall accuracy below 40%. This low accuracy raises serious concerns about the risk of propagating incorrect scientific findings when blindly relying on AI-generated analyses. Therefore, we develop an AI agent that begins with and iteratively refines an analysis plan before generating code, achieving 74% accuracy. We embody this insight in a platform that enables users to codevelop analysis plans with LLMs and execute them within an integrated environment. In a user study with five medical researchers, the platform enabled users to complete over 80% of the analysis code for three studies.
Indexed as
Identifiers
41571796What 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.