Evidence map›Paper›PMID 39911582›Full record

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.

David Hua, Neysa Petrina, Alan J Sacks, Noel Young, Jin-Gun Cho, Ross Smith, Simon K Poon

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

David HuaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0000-0002-6334-5626
Neysa PetrinaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0000-0001-9163-1761
Alan J SacksOur Medical Radiology, Sydney, NSW 2065, Australia.ORCID https://orcid.org/0000-0002-4786-6771
Noel YoungLumus Imaging, Sydney, NSW 2000, Australia.ORCID https://orcid.org/0000-0001-8591-8897
Jin-Gun ChoLumus Imaging, Sydney, NSW 2000, Australia.ORCID https://orcid.org/0000-0002-0735-4691
Ross SmithSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.
Simon K PoonSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0000-0003-2726-9109

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceevaluationhuman factorspulmonary tuberculosistechnical performancetranslational impact

Identifiers

PMID39911582
PMCPMC11796096

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.