ArticleProceedings of the National Academy of Sciences of the United States of America2025
AI assistance improves people's ability to distinguish correct from incorrect eyewitness lineup identifications.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- AI-driven transformation in forensic medicine education: applications, pedagogical shifts, and future challenges.Frontiers in medicine · 2026Review
- Suspect identification accuracy from lineups, in the lab and in the field.Cognitive research: principles and implications · 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
4 authors.
Funding
Abstract
Mistaken eyewitness identification is one of the leading causes of false convictions. Improving law enforcement's ability to identify correct identifications could have profound implications for criminal justice. Across two experiments, we show that AI-assistance can improve people's ability to distinguish between accurate and inaccurate eyewitness lineup identifications. Participants (Experiment 1:
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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.