ArticleNPJ digital medicine2025
Empirically derived evaluation requirements for responsible deployments of AI in safety-critical settings.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Human-Centered design requirements for clinical decision support in cardiac surgery perfusion.Human factors in healthcare · 2026Article
- The Double-Edged Sword Effect of AI Application Among Clinical Nurses Based on the Job Demands-Resources Model: Qualitative Study.JMIR nursing · 2026Article
- Artificial intelligence literacy in nursing: a concept analysis.Frontiers in public health · 2026Review
- Role reconstruction and competency requirements of emergency nurses in the human-machine collaborative mode: a qualitative study.Frontiers in public health · 2026Article
- Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Processes to assure the safe, effective, and responsible deployment of artificial intelligence (AI) in safety-critical settings are urgently needed. Here we show a procedure to empirically evaluate the impacts of AI augmentation as a basis for responsible deployment. We evaluated three augmentative AI technologies nurses used to recognize imminent patient emergencies, including combinations of AI recommendations and explanations. The evaluation involved 450 nursing students and 12 licensed nurses assessing 10 historical patient cases. With each technology, nurses' performance was both improved and degraded when the AI algorithm was most correct and misleading, respectively. Our findings caution that AI capabilities alone do not guarantee a safe and effective joint human-AI system. We propose two minimum requirements for evaluating AI in safety-critical settings: (1) empirically measure the performance of people and AI together and (2) examine a range of challenging cases which produce a range of strong, mediocre, and poor AI performance.
Identifiers
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.