ArticleJAMIA open2025
Towards human-AI collaboration in radiology: a multidimensional evaluation of the acceptability of AI for chest radiograph analysis in supporting pulmonary tuberculosis diagnosis.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Clinician Trust and Human Factors in AI-Enabled Clinical Decision Support in Acute Care: Mixed Methods Study.JMIR human factors · 2026Article
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Research overview-2025: integrating scientific progress to accelerate global tuberculosis elimination.Frontiers in tuberculosis · 2026Article
- Prioritizing human-AI collaboration in healthcare: the TRIAD framework for trustworthy governance, real-world, and integrated adaptive deployment.Military Medical Research · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Artificial intelligence (AI) technology promises to be a powerful tool in addressing the global health challenges posed by tuberculosis (TB). However, evidence for its real-world impact is lacking, which may hinder safe, responsible adoption. This case study addresses this gap by assessing the technical performance, usability and workflow aspects, and health impact of implementing a commercial AI system (qXR by Qure.ai) to support Australian radiologists in diagnosing pulmonary TB. Materials and Methods: A retrospective diagnostic accuracy evaluation was conducted to establish the technical performance of qXR in detecting TB compared to a human radiologist and microbiological reference standard. A qualitative human factors assessment was performed to investigate the user experience and clinical decision-making process of radiologists using qXR. A task productivity analysis was completed to quantify how the radiological screening turnaround time is impacted. Results: qXR displays near-human performance satisfying the World Health Organization's suggested accuracy profile. Radiologists reported high satisfaction with using qXR based on minimal workflow disruptions, respect for their professional autonomy, and limited increases in workload burden despite poor algorithm explainability. qXR delivers considerable productivity gains for normal cases and optimizes resource allocation through redistributing time from normal to abnormal cases. Discussion and Conclusion: This study provides preliminary evidence of how an AI system with reasonable diagnostic accuracy and a human-centered user experience can meaningfully augment the TB diagnostic workflow. Future research needs to investigate the impact of AI on clinician accuracy, its relationship with efficiency, and best practices for optimizing the impact of clinician-AI collaboration.
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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.