ArticleNature human behaviour2023
Accelerating science with human-aware artificial intelligence.
Article in Nature human behaviour, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Review
- Evaluating LLMs' divergent thinking capabilities for scientific idea generation with minimal context.Nature communications · 2026Article
- PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science.Scientific data · 2025Article
- Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.Journal of the Royal Society, Interface · 2025Article
- Mutual benefits of social learning and algorithmic mediation for cumulative culture.Journal of the Royal Society, Interface · 2025Article
- Static network structure cannot stabilize cooperation among large language model agents.PloS one · 2025Article
- Generative AI and academic scientists in US universities: Perception, experience, and adoption intentions.PloS one · 2025Article
- Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data.PloS one · 2025Article
- Quantifying the use and potential benefits of artificial intelligence in scientific research.Nature human behaviour · 2024Article
- Network community detection via neural embeddings.Nature communications · 2024Article
- A systematic review of AI literacy scales.NPJ science of learning · 2024Article
- Advanced Design of Soft Robots with Artificial Intelligence.Nano-micro letters · 2024Review
- Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.BMC bioinformatics · 2024Article
- Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology.Journal of proteome research · 2024Review
- Large Language Models and the Wisdom of Small Crowds.Open mind : discoveries in cognitive science · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences that are cognitively accessible to experts dramatically improves (by up to 400%) AI prediction of future discoveries beyond models focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising 'alien' hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. By accelerating human discovery or probing its blind spots, human-aware AI enables us to move towards and beyond the contemporary scientific frontier.
Indexed as
Identifiers
37443269What 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.