ReviewRoyal Society open science2025
Autonomous 'self-driving' laboratories: a review of technology and policy implications.
Review in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Automated Photothermal Control of Block Copolymer Self‑Assembly and Metal Oxide Nanostructures Sintering.Macromolecular rapid communications · 2026Article
- A Narrative Review of Artificial Intelligence for Drug Repurposing: Lessons From COVID-19 and Oncology (2020-2025).CPT: pharmacometrics & systems pharmacology · 2026Review
- An autonomous lab for data-driven homogeneous catalysis.Nature communications · 2026Article
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Self-driving lab for the data-driven design of single-chain polymer nanoparticles.Cell press blue · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
- From capability uplift to capability governance: an AI-biosecurity stack.Frontiers in microbiology · 2026Article
- Automated Laboratory Security Tiers: a framework for evaluating and mitigating biosecurity risks from latent capabilities.Frontiers in microbiology · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Design biology and mind-body health.Journal, genetic engineering & biotechnology · 2025Review
- Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook.International journal of molecular sciences · 2025Review
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
No grant is acknowledged in the PubMed record.
Abstract
This article reviews and provides perspective on the emerging technology of autonomous, 'self-driving' laboratories (SDLs) that combine artificial intelligence (AI) and laboratory automation to perform research in chemistry, materials science and biological sciences. Today's most capable SDLs automate nearly the entire scientific method, from hypothesis generation, experimental design, experiment execution and data analysis, to drawing conclusions and updating hypotheses for subsequent rounds of optimization or discovery. 'Cloud labs' offer subscription-based remote-control access to experimental capabilities. Reports of AI-directed experiments executed in cloud labs are appearing in the literature, previewing a democratization of science that intrigues but inspires concern. Indeed, SDLs have potential implications for society far beyond the academy. Inventions emerging from AI-driven science pose a grand challenge, as patent laws across the world recognize only human inventors. If the inventions they generate remain unpatentable, funding for SDLs may be constrained. SDLs raise safety and security concerns. We deem them surmountable with a proactive approach, ultimate human accountability and robust cybersecurity measures. Finally, we estimate the impacts of SDLs on the technical labour force. Our analysis suggests that SDLs may displace some scientific roles but are likely to create many new opportunities.
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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.