ReviewNPJ digital medicine2022
AI in the hands of imperfect users.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.BMC medical ethics · 2024Pooled it
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Rethinking Ethics for an Era of Trusted Computational Tools.The Psychiatric clinics of North America · 2026Review
- The 'Hippocratic Oath' for AI-based clinical decision support systems.BMC medical informatics and decision making · 2026Article
- Article
- Artificial intelligence in primary care: innovation at a crossroads.The Lancet. Primary care · 2026Review
- Understanding the influence of design-related factors on human-AI teaming in a face matching task.Cognitive research: principles and implications · 2026Article
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- To err is no more (only) human: where does legal medicine stands on?International journal of legal medicine · 2026Article
- Stakeholder Criteria for Trust in Artificial Intelligence-Based Computer Perception Tools in Health Care: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- Beyond models: a paradigm shift toward human-centered AI system design.Critical care (London, England) · 2025Article
- Comparing Generative Artificial Intelligence and Mental Health Professionals for Clinical Decision-Making With Trauma-Exposed Populations: Vignette-Based Experimental Study.JMIR mental health · 2025Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Robustness tests for biomedical foundation models should tailor to specifications.NPJ digital medicine · 2025Article
- Guiding responsible AI in healthcare in the Philippines.NPJ digital medicine · 2025Review
- Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.Critical care explorations · 2025Article
- Association between heatwave exposure and rapid kidney function decline: a longitudinal cohort study from CHARLS.BMC public health · 2025Article
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
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
Abstract
As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML's human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.
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.