Lung CancerNCT06988579
The Impact of AI Assistance on Radiologist Performance and Healthcare Costs in LDCT-Based Lung Cancer Screening
A study testing AI in people with Lung Cancer and Artificial Intelligence (AI), run by The First Affiliated Hospital of Guangzhou Medical University at 1 site in China.
Trial summary
AI diagnostic systems show great promise for improving lung cancer screening in community healthcare settings. While not originally designed for primary care, these tools demonstrate capabilities in nodule detection and workflow optimization. Read more about this study
- Who is it for?
- Ages 40 to 74 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer, Artificial Intelligence (AI), Randomized Controlled Trial
- What is being tested?
- AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 7,294 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2024to 7 Jan 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 74 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Aged 45-74 years
- Permanent resident of participating study communities
- No prior history of lung cancer and no lung cancer screening within the past 3 months
- Able to comprehend and voluntarily sign informed consent, with willingness to participate in long-term follow-up
Who cannot join
- Individuals with a confirmed diagnosis of lung cancer
- Those with severe comorbidities contraindicating CT imaging
- Inability to understand study protocols or provide informed consent due to cognitive impairment
- Concurrent participation in other clinical trials that may interfere with study outcomes
- Unable to comply with follow-up requirements
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
the First Affiliated Hospital of Guangzhou Medical University,
Guangzhou, Guangdong, 510120
Jianxing He, +8618320729913, drjianxing.he@gmail.com
About this study
Official title: Evaluation of AI-Assisted Versus Conventional Human Reading for Lung Cancer Screening in Community-Based Settings: A Randomized Controlled Trial
AI diagnostic systems show great promise for improving lung cancer screening in community healthcare settings. While not originally designed for primary care, these tools demonstrate capabilities in nodule detection and workflow optimization. However, their effectiveness in resource-limited community centers requires thorough evaluation.
This RCT compares AI-assisted versus manual CT interpretation across community health centers. Expert radiologists will establish reference standards, while an independent committee blindly evaluates cases from both groups. The study assesses diagnostic accuracy, operational efficiency, and cost-effectiveness, with blinded analysts resolving discrepancies through consensus to ensure reliable results.
Read the detailed description
Artificial intelligence (AI) technologies, particularly advanced medical imaging analysis systems like the AI diagnostic platform evaluated in this study, demonstrate significant potential for enhancing lung cancer screening programs in community healthcare settings.Although this AI system was not originally designed specifically for primary care implementation, it has shown promising capabilities in various clinical applications, including nodule detection, malignancy risk stratification, and workflow optimization in radiology departments. However, its effectiveness in improving screening accuracy and operational efficiency in resource-limited community health centers remains to be thoroughly investigated.
Lung cancer screening and diagnosis involve complex clinical processes,including image interpretation, risk factor assessment, and follow-up decision-making. Implementing AI tools like this diagnostic platform in community screening programs could potentially improve detection rates, standardize interpretations, and optimize resource allocation. Nevertheless, the system has not been rigorously validated for use in primary care settings and may carry limitations in generalizability across diverse patient populations and imaging equipment variations. Inappropriate implementation could lead to diagnostic errors or inefficient resource utilization. Therefore, evaluating how such AI systems can effectively support community-based screening while maintaining diagnostic accuracy and cost-effectiveness is of paramount importance.
In this randomized controlled trial, participating community health centers will be allocated to either an AI-assisted interpretation group or a conventional manual interpretation group. All screening cases will undergo standardized low-dose CT imaging, with results interpreted through the respective group's designated method. A panel of three expert radiologists will establish reference standards for all cases, while an independent review committee will blindly evaluate a subset of cases from both groups to assess interpretation consistency. The evaluation will focus on diagnostic performance metrics, operational efficiency parameters, and cost-effectiveness indicators. Two separate analyst teams, blinded to group assignments, will process and compare the outcomes using predefined statistical methods, with any discrepancies resolved through consensus discussions to ensure data reliability.
- Being tested
- AI
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIOther
An integrated AI-human collaborative workflow for lung cancer screening interpretation
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Group | Experimental | The AI-assisted group utilized AI-powered diagnostic software for interpreting low-dose computed tomography (LDCT) scans Receives: Other: AI |
| The manual interpretation group | No intervention | The manual interpretation group relied on standard radiologist evaluation for analyzing low-dose computed tomography (LDCT) scans. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy
Sensitivity and Specificity: Comparison of AI-assisted versus manual interpretation in detecting malignant pulmonary nodules, validated against histopathological confirmation or 12-month clinical follow-up.
Early Detection Rate: Proportion of stage I/II lung cancers correctly identified by each method.
Measured: One year after entry
Interpretation Consistency
Inter-reader Agreement: Measured by Cohen's kappa (κ) between AI-assisted radiologists and the independent review committee (IRC).
Intra-reader Variability: Consistency of nodule classification in repeat readings (subset analysis).
Measured: One year after entry
Secondary outcomes (1)
Cost-Effectiveness
Resource Utilization: Comparative analysis of staffing, equipment, and follow-up costs per detected cancer case.
Incremental Cost per QALY (Quality-Adjusted Life Year): Long-term economic impact modeling.
Measured: One year after entry
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 May 2025
- Study start1 Jul 2024
- Primary completion31 Oct 2025 (estimated)
- Study completion7 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital of Guangzhou Medical UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 7,294 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jianxing He, The First Affiliated Hospital of Guangzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06988579
- Sponsor's protocol ID
- ES-2024-193-01-002
- Status last verified
- May 2025
- First submitted
- 16 May 2025
- Last updated
- 26 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of AI-Assisted Versus Conventional Human Reading for Lung Cancer Screening in Community-Based Settings: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT06988579
- Acronym / sponsor ID
- ES-2024-193-01-002
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 7,294 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Guangzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2024 (actual)
- Primary completion
- 31 Oct 2025 (estimated)
- Study completion
- 7 Jan 2026 (estimated)
- First posted
- 25 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
AI diagnostic systems show great promise for improving lung cancer screening in community healthcare settings. While not originally designed for primary care, these tools demonstrate capabilities in nodule detection and workflow optimization. However, their effectiveness in resource-limited community centers requires thorough evaluation.
This RCT compares AI-assisted versus manual CT interpretation across community health centers. Expert radiologists will establish reference standards, while an independent committee blindly evaluates cases from both groups. The study assesses diagnostic accuracy, operational efficiency, and cost-effectiveness, with blinded analysts resolving discrepancies through consensus to ensure reliable results.
Detailed description
Artificial intelligence (AI) technologies, particularly advanced medical imaging analysis systems like the AI diagnostic platform evaluated in this study, demonstrate significant potential for enhancing lung cancer screening programs in community healthcare settings.Although this AI system was not originally designed specifically for primary care implementation, it has shown promising capabilities in various clinical applications, including nodule detection, malignancy risk stratification, and workflow optimization in radiology departments. However, its effectiveness in improving screening accuracy and operational efficiency in resource-limited community health centers remains to be thoroughly investigated.
Lung cancer screening and diagnosis involve complex clinical processes,including image interpretation, risk factor assessment, and follow-up decision-making. Implementing AI tools like this diagnostic platform in community screening programs could potentially improve detection rates, standardize interpretations, and optimize resource allocation. Nevertheless, the system has not been rigorously validated for use in primary care settings and may carry limitations in generalizability across diverse patient populations and imaging equipment variations. Inappropriate implementation could lead to diagnostic errors or inefficient resource utilization. Therefore, evaluating how such AI systems can effectively support community-based screening while maintaining diagnostic accuracy and cost-effectiveness is of paramount importance.
In this randomized controlled trial, participating community health centers will be allocated to either an AI-assisted interpretation group or a conventional manual interpretation group. All screening cases will undergo standardized low-dose CT imaging, with results interpreted through the respective group's designated method. A panel of three expert radiologists will establish reference standards for all cases, while an independent review committee will blindly evaluate a subset of cases from both groups to assess interpretation consistency. The evaluation will focus on diagnostic performance metrics, operational efficiency parameters, and cost-effectiveness indicators. Two separate analyst teams, blinded to group assignments, will process and compare the outcomes using predefined statistical methods, with any discrepancies resolved through consensus discussions to ensure data reliability.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Group | Experimental | Other: AIThe AI-assisted group utilized AI-powered diagnostic software for interpreting low-dose computed tomography (LDCT) scans |
| The manual interpretation group | No intervention | –The manual interpretation group relied on standard radiologist evaluation for analyzing low-dose computed tomography (LDCT) scans. |
Who may qualify
- Ages
- 40 to 74 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Aged 45-74 years
- Permanent resident of participating study communities
- No prior history of lung cancer and no lung cancer screening within the past 3 months
- Able to comprehend and voluntarily sign informed consent, with willingness to participate in long-term follow-up
Key exclusion criteria (5 listed)
- Individuals with a confirmed diagnosis of lung cancer
- Those with severe comorbidities contraindicating CT imaging
- Inability to understand study protocols or provide informed consent due to cognitive impairment
- Concurrent participation in other clinical trials that may interfere with study outcomes
- Unable to comply with follow-up requirements
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AIAn integrated AI-human collaborative workflow for lung cancer screening interpretation | Other | AI-Assisted Group |
Primary outcome
Diagnostic Accuracy
Time frame: One year after entry
Sensitivity and Specificity: Comparison of AI-assisted versus manual interpretation in detecting malignant pulmonary nodules, validated against histopathological confirmation or 12-month clinical follow-up. Early Detection Rate: Proportion of stage I/II lung cancers correctly identified by each method.
Interpretation Consistency
Time frame: One year after entry
Inter-reader Agreement: Measured by Cohen's kappa (κ) between AI-assisted radiologists and the independent review committee (IRC). Intra-reader Variability: Consistency of nodule classification in repeat readings (subset analysis).
1 secondary outcome
Cost-Effectiveness
Time frame: One year after entry
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI in people with Lung Cancer and Artificial Intelligence (AI), run by The First Affiliated Hospital of Guangzhou Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 26 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jianxing He (+8618320729913, drjianxing.he@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy (time frame: One year after entry). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jun 2025.