ReviewNature2023
Scientific discovery in the age of artificial intelligence.
Review in Nature, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 398 papers, 2 of them syntheses 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
398 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Systematic review and meta-analysis of PET-based prognostic metrics in CAR-T treatment of DLBCL.Frontiers in immunology · 2026Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal dialysis.Renal failure · 2026Article
- From promise to practice: digital health through the lens of neonatal care.Lancet regional health. Americas · 2026Review
- Comparison of DeepSeek AI and Human Reviewers in Structured Evaluation of Neurosurgical Manuscripts: A Pilot Study.Journal of neurological surgery reports · 2026Article
- A ScNature materials · 2026Article
- From Clipping to Coiling and Beyond: Mapping the Evolution and Future Directions of Intracranial Aneurysm Research.Brain sciences · 2026Article
- Unlocking Sensitive Data with SPHERE in the Age of AI.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- AResKGLM: a graph-grounded language-model framework for interpretable multi-hop antimicrobial resistance reasoning.Briefings in bioinformatics · 2026Article
- Alignment Between Large Language Models and Consensus in Endoscopic Spinal Disc Surgery: A Comparative Analysis Against the Neurocore-SENSED.JOR spine · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Crosstalk Between CTSBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-centric feedback loops for next-generation immunotherapy development.Nature biomedical engineering · 2026Review
- Truth, tone, and trust: integrating an AI exercise to foster responsible science communication in a graduate biomedical science course.Journal of microbiology & biology education · 2026Article
- Materials Databases: Foundations of Modern Digital Materials.Precision chemistry · 2026Review
- Weakly Supervised MRI-Based Classification of Alzheimer's Disease Using Clinical Pseudo-Labels.NeuroSci · 2026Article
- AI-driven robotics for optics.Science advances · 2026Article
- The rise of large language models and the direction and impact of US federal research funding.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
338 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
30 authors.
Funding
Abstract
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
Indexed as
Identifiers
37532811What 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.