Evidence map›Paper›PMID 41876649›Full record

Trial reportNature medicine2026

AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trial.

Nick Woznitza, Lesley Smith, Janette Rawlinson, Iain Au-Yong, Bindu George, Madava G Djearaman, Arjun Nair, Richard W Lee, Neal Navani, Siyabonga Ndwandwe and 8 more

Registry-linked trialAbstract readRandomized Controlled TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07768085 (A Multicenter Randomized Controlled Trial of AI-Assisted Reading Compared With Standard Radiology Interpretation for Malignant Hepatic Lesions on Contrast-Enhanced CT), which is not on this map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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.

NCT07768085 nanot yet recruitingnot on this map

A Multicenter Randomized Controlled Trial of AI-Assisted Reading Compared With Standard Radiology Interpretation for Malignant Hepatic Lesions on Contrast-Enhanced CT: Diagnostic, Clinical and Workflow Outcomes

TypeinterventionalSponsorShengjing HospitalRan2026 to 2027Enrolled40,000ConditionsHepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (Icc), Hepatic Metastasis, Hepatic HemangiomaArmsAI-assisted abdominal CE-CT reporting, Standard abdominal CE-CT reporting without AI assistance
3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Nick WoznitzaUniversity College London Hospitals NHS Foundation Trust, London, UK.ORCID http://orcid.org/0000-0001-9598-189X
Lesley SmithLeeds Cancer Research UK Clinical Trials Unit, University of Leeds, Leeds, UK.
Janette Rawlinson, Tipton, UK.ORCID http://orcid.org/0000-0002-9536-9809
Iain Au-YongDepartment of Radiology, Nottingham University Hospitals, Nottingham, UK.ORCID http://orcid.org/0009-0001-6422-7752
Bindu GeorgeDepartment of Radiology, Nottingham University Hospitals, Nottingham, UK.ORCID http://orcid.org/0009-0007-8006-9575
Madava G DjearamanRadiology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.ORCID http://orcid.org/0000-0003-3408-1411
Arjun NairUniversity College London Hospitals NHS Foundation Trust, London, UK.
Richard W LeeThe Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, London, UK.ORCID http://orcid.org/0000-0001-8368-3406
Neal NavaniLungs for Living Research Centre, University College London, London, UK.ORCID http://orcid.org/0000-0002-6412-7516
Siyabonga NdwandweResearch Department of Primary Care and Population Health, University College London, London, UK.
Caroline S ClarkeResearch Department of Primary Care and Population Health, University College London, London, UK.ORCID http://orcid.org/0000-0002-4676-1257
Andrew CreedenRadiology Department, University Hospitals of Leicester NHS Trust, Leicester, UK.
Josh NewsomeRadiology Department, University Hospitals of Leicester NHS Trust, Leicester, UK.
Indrajeet DasRadiology Department, University Hospitals of Leicester NHS Trust, Leicester, UK.
Sylvia AbaokporoDepartment of Radiology, Nottingham University Hospitals, Nottingham, UK.ORCID http://orcid.org/0009-0001-5031-111X
Richard TuckerDepartment of Radiology, Nottingham University Hospitals, Nottingham, UK.
James HathornRadiology Department, East Suffolk and North East Essex NHS Foundation Trust, Colchester, UK.
David R BaldwinRespiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK. david.baldwin12@nhs.net.ORCID http://orcid.org/0000-0001-8410-7160

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prioritizing artificial intelligence (AI)-detected imaging findings may reduce the time to diagnosis of lung cancer. This prospective, multicentre, randomized controlled trial tested whether immediate AI prioritization of primary care-requested chest X-rays (CXR) influenced time to computed tomography (CT) and lung cancer diagnosis, the primary outcomes. Secondary outcomes included the number of urgent suspected lung cancer referrals, incidence and stage of lung cancer, times to urgent referral and treatment, concordance between AI and radiology reports, and algorithm accuracy. AI was available in both study arms, with AI prioritization randomized by day. Of 97,731 participant CXRs, 4,405 were excluded due to data compliance issues or failure of randomization, resulting in 93,326 CXRs analyzed (45,987 and 47,339 in the prioritization 'on' or 'off' arms, respectively). A total of 13,347 CTs were identified, with 2,766 performed within 14 days of CXR. Median (interquartile range) times to CT were 53 days (17-145) and 53 days (19-141), with and without AI prioritization, corresponding to a ratio of geometric means of 0.97 (95% confidence interval (CI) = 0.93-1.02; P = 0.31). When restricted to CTs performed within 14 days of CXR, the median time to CT was 8 days (5-11) in both groups. Lung cancer was diagnosed in 558 people (0.6% of CXRs). Median times to diagnosis were 44 days (26-90) and 46 days (24-105) respectively, with a ratio of geometric means of 0.98 (95% CI = 0.83-1.16; P = 0.84). No significant differences were observed in time to lung cancer referral (14 versus 15 days; P = 0.13), time to treatment (76 versus 72.5 days; P = 0.99) or stage at diagnosis (P = 0.34). Discordance between AI and radiology reports occurred in 28,261 CXRs (30.3%) and expert radiology review identified actionable findings in 6,750 cases (23.9%). AI prioritization of CXR requested by UK primary care has no significant impact on the lung cancer pathway. Therefore, CXR AI deployments should not include worklist prioritization in this context. Future research should differentiate between primary pathway changes and the direct impact of AI. ISRCTN registration: 78987039 .

Indexed as

Artificial IntelligenceLung NeoplasmsRadiography, ThoracicAgedAlgorithmsFemaleHumansMaleMiddle AgedProspective StudiesReferral and ConsultationTomography, X-Ray Computed

Identifiers

PMID41876649
PMCPMC13190311

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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.