ReviewNature reviews. Physics2022
On scientific understanding with artificial intelligence.
Review in Nature reviews. Physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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
68 citing papers in PubMed.
- Review
- AI-driven robotics for optics.Science advances · 2026Article
- A broadband lithium tantalate-on-silicon nitride heterogeneous modulator for optical and terahertz communications and radar sensing.Nature communications · 2026Article
- Explainable AI: learning from the learners.Nature communications · 2026Review
- A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism.Journal of food science · 2026Article
- Moving from table to graph in physics-informed spatio-temporal symbolic regression.Scientific reports · 2026Article
- Data-driven design and screening of novelRSC advances · 2026Article
- AI for scientific discovery is a social problem.Patterns (New York, N.Y.) · 2026Review
- Interpretable machine learning for shoreline forecasting.Scientific reports · 2026Article
- Autonomous artificial intelligence, scientific research, and human values.AI and ethics · 2026Article
- Accelerating primer design for amplicon sequencing using large language model-powered agents.Nature biomedical engineering · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Knowledge integration for physics-informed symbolic regression using pre-trained large language models.Scientific reports · 2026Article
- Sub-picojoule-per-bit volitional neuromorphic devices for precise targeting and tracking.Nature communications · 2026Article
- Technological Understanding: On the cognitive skill involved in the design and use of technological artefacts.Synthese · 2026Article
- Multimodal In-Sensor Computing with Dual-Phase Organic Synapses for Wearable Fitness Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Unsupervised classification of non-Hermitian topological phases under symmetries.National science review · 2026Article
- Characterizing LLM scientific concept generation: A multi-dimensional measurement study.PloS one · 2026Article
- Heavy-tailed update distributions arise from information-driven self-organization in nonequilibrium learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.Science advances · 2025Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.
Indexed as
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What 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.