ArticleNature human behaviour2025
Large language models surpass human experts in predicting neuroscience results.
Article in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
What it found
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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.
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Who cites it
50 citing papers in PubMed.
- Spatiotemporal disarray of inflammatory microenvironment following spinal cord injury.Neural regeneration research · 2026Article
- Disentangling interaction and bias effects in opinion dynamics of large language models.Nature communications · 2026Article
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- Persona-prompted LLM agents achieve modest but genuine prediction of human social media reactions.Scientific reports · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Propagation and preservation of AI-discovered problem-solving strategies in human culture.Nature communications · 2026Article
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- Leveraging generative artificial intelligence for the development of non-interventional research study protocols: a proof-of-concept feasibility study.BMC medical research methodology · 2026Article
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
- Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature.Research square · 2026Article
- A systematic review and meta-analysis of psychological and behavioural responses in human-agent vs. human-human interactions.Communications psychology · 2026Article
- St. Gallen International Breast Cancer Consensus-Based Clinical Decision Validation: Concordance Assessment Between Deep Large Language Model Outputs and Global Expert Panel Recommendations.Annals of surgical oncology · 2026Article
- SpikeLab: Agentic tools for spike data analysis.bioRxiv : the preprint server for biology · 2026Article
- Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs.BMJ health & care informatics · 2026Article
- Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit.BMJ open · 2026Article
- Advancing the science of qualitative patient preference assessment using large language models.PLOS digital health · 2026Article
- Beyond human gold standards: A multimodel framework for automated abstract classification and information extraction.Research synthesis methods · 2026Article
- Confidence-weighted integration of human and machine judgments for superior decision-making.Patterns (New York, N.Y.) · 2026Article
- Benchmarking Large Language Models for Drug Combination Alerts: Achieving Expert-Level Reliability via Knowledge Grounding and Contextual Reasoning.Journal of medicinal chemistry · 2026Article
Corrections and comments
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Authors and funding
39 authors.
Funding
Abstract
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs indicated high confidence in their predictions, their responses were more likely to be correct, which presages a future where LLMs assist humans in making discoveries. Our approach is not neuroscience specific and is transferable to other knowledge-intensive endeavours.
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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.