ArticleCities (London, England)2026
Artificial Intelligence in urban design: A systematic review.
Article in Cities (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Artificial Intelligence (AI) is playing an increasingly transformative role in urban design by enhancing the efficiency, scalability, and adaptability of design processes. This study presents a systematic review of AI applications in urban design, with a particular focus on the design generation phase, encompassing data analysis, scheme generation and optimization, and design visualization. AI-driven methodologies facilitate rapid data processing, automated design iterations, and advanced visualizations, thereby mitigating some key limitations in conventional urban design workflows that often rely on manual and time-consuming processes. Despite these advancements, several challenges persist. These include the fragmented integration of AI tools into existing workflows, the incomplete automation of the design process, and the potential for algorithmic bias in AI-generated outcomes. Such shortcomings underscore the importance of developing structured AI workflows, fostering effective human-AI collaboration, and curating diverse, inclusive datasets to promote equitable and context-sensitive design solutions. This review advocates for a balanced approach that leverages AI's computational power while retaining human creativity and contextual judgment. By doing so, AI-enhanced urban design holds the potential to support the creation of more sustainable, efficient, and resilient cities, better equipped to meet the complex challenges of contemporary urbanization.
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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.