ArticleNPJ digital medicine2025
The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance.
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 8 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
8 citing papers in PubMed.
- Associations between echocardiographic traits and artificial intelligence-enabled electrocardiography predictions of heart failure.European heart journal. Digital health · 2026Article
- NEXIM: A Nash Equilibrium-Based Framework for Stable Explainable AI in Medical Applications.medRxiv : the preprint server for health sciences · 2026Article
- Pediatric Autism Diagnosis Accuracy and Confidence: A Comparison of Experienced and Inexperienced Clinicians Making Decisions with and without AI Decision Support.Research square · 2026Article
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology.Diagnostics (Basel, Switzerland) · 2026Review
- Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence and Innovation in Oral Health Care Sciences: A Conceptual Review.Healthcare (Basel, Switzerland) · 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
5 authors.
Funding
Abstract
Explainable Artificial Intelligence (XAI) is proposed as essential for high-risk applications like healthcare, where it aims to enhance user trust. However, studies often rely on automated metrics rather than user evaluation. We adapt a prototype-based XAI model for image-based gestational age (GA) estimation and evaluate its impact on trust, reliance, and performance, including a novel measure of appropriate reliance. Ten sonographers completed a 3-stage reader study assessing the XAI model's impact on GA estimates. Model predictions reduced clinician mean absolute error (MAE) from 23.5 to 15.7 days, and explanations had a further non-significant reduction to 14.3 days. However, the impact of explanations varied across participants, with some performing worse with explanations than without. Additionally, although explanations increased participant confidence, they had no significant effect on trust or reliance on the model. These counterintuitive results highlight potential pitfalls in deploying XAI, emphasising the need for human studies to capture clinician variability.
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.