Pulmonary NoduleAI-NoduleCareNCT07841600
AI-Assisted Management of Pulmonary Nodules Found on Low-Dose CT in Health Screening
A study testing AI-Assisted Pulmonary Nodule Reporting Workflow and Conventional Pulmonary Nodule Reporting Workflow in people with Pulmonary Nodule and Clinical Decision Support, run by The First Affiliated Hospital of Guangzhou Medical University.
Trial summary
Pulmonary nodules are frequently found during low-dose computed tomography (LDCT) health screening. The main challenge is not only detecting nodules, but also recommending the appropriate next step, such as routine follow-up, short-interval imaging follow-up, or specialist evaluation. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pulmonary Nodule, Clinical Decision Support
- What is being tested?
- AI-Assisted Pulmonary Nodule Reporting Workflow and Conventional Pulmonary Nodule Reporting Workflow
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Oct 2026to 31 Dec 2028
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age 18 years or older.
- Undergoing chest low-dose computed tomography at a participating health examination branch during the study recruitment period.
- At least one pulmonary nodule is identified on the index LDCT and requires risk stratification and a management decision regarding follow-up, repeat imaging, or specialist evaluation.
- Thin-section reconstructed images are of sufficient quality for clinical interpretation and, where applicable, AI analysis.
- Required clinical risk information is available, including age, sex, smoking history, history of malignancy, family history of lung cancer, and available prior chest imaging.
- Included under the ethics committee-approved consent, simplified notification, waiver, and/or opt-out process, with no documented refusal of research data use.
Who cannot join
- Previously diagnosed lung cancer or currently receiving lung cancer-related treatment.
- Known pulmonary metastasis from another malignancy.
- Imaging findings that clearly require immediate entry into a lung cancer specialty diagnostic or treatment pathway and are not appropriate for routine pulmonary nodule risk-stratified management.
- An urgent thoracic condition requiring immediate management, such as pneumothorax, large pleural effusion, or acute pulmonary embolism.
- Severe imaging artifact, incompatible slice thickness or reconstruction, or a lesion type, imaging parameter, or disease extent outside the prespecified locked scope of the AI system.
- Previous enrollment in this study.
- Explicit refusal of research data use, or another reason judged by the investigator to make inclusion inappropriate.
About this study
Official title: Clinical Utility of Artificial Intelligence-Assisted Management Decisions for Pulmonary Nodules Detected by Low-Dose Computed Tomography in Health Screening: A Multicenter, Prospective, Cluster-Randomized Controlled Trial
Pulmonary nodules are frequently found during low-dose computed tomography (LDCT) health screening. The main challenge is not only detecting nodules, but also recommending the appropriate next step, such as routine follow-up, short-interval imaging follow-up, or specialist evaluation. This multicenter cluster-randomized trial will evaluate whether an artificial intelligence (AI)-assisted reporting workflow improves the appropriateness of pulmonary nodule management decisions in health examination settings without increasing under-management or missed referrals. Participating health examination branches, rather than individual participants, will be randomly assigned in a 1:1 ratio to a conventional reporting workflow or an AI-assisted reporting workflow. All final reports will be reviewed and signed by qualified physicians. An independent expert endpoint committee, blinded to study assignment and AI output, will determine the acceptable management range for each case. Approximately 2,000 adults with pulmonary nodules detected on LDCT will be included across at branches.
- Conditions
- Pulmonary Nodule, Clinical Decision Support
- Being tested
- AI-Assisted Pulmonary Nodule Reporting Workflow, Conventional Pulmonary Nodule Reporting Workflow
- Keywords
- Artificial Intelligence, Low-Dose Computed Tomography, Pulmonary Nodule Management, Lung Cancer Screening, Human-AI Collaboration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted Pulmonary Nodule Reporting WorkflowOther
A locked-version artificial intelligence decision-support workflow applied after the physician records an initial assessment. The system displays pulmonary nodule location, size, density, morphologic features, estimated malignancy risk, and a suggested management category. The physician retains final responsibility. If the system fails, times out, or produces an abnormal output, the case returns to manual reporting. No online learning or automatic model updating is allowed during the trial.
Conventional Pulmonary Nodule Reporting WorkflowOther
Standard manual LDCT interpretation and pulmonary nodule management recommendation by qualified physicians using the participating branch's routine reporting process, without access to study AI output.
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Pulmonary Nodule Reporting Workflow | Experimental | Physicians first record and lock an initial pulmonary nodule management decision without viewing AI results. They then review locked-version AI outputs, including nodule characteristics, estimated malignancy risk, and a management recommendation, and issue the final physician-signed report. Physicians may accept, modify, or reject the AI recommendation. The AI cannot automatically sign reports or directly instruct participants. Receives: Other: AI-Assisted Pulmonary Nodule Reporting Workflow |
| Conventional Pulmonary Nodule Reporting Workflow | Active comparator | Physicians interpret LDCT examinations and issue pulmonary nodule management recommendations using the participating branch's conventional clinical reporting workflow. Study AI output is not displayed. Receives: Other: Conventional Pulmonary Nodule Reporting Workflow |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of Participants With an Appropriate Final Management Recommendation
Percentage of evaluable participants whose final physician recommendation is within the acceptable management range determined by the blinded independent expert endpoint committee. Recommendations are classified as routine or annual follow-up, short-interval imaging follow-up, or specialist evaluation/referral. The expert committee will complete adjudication using index data within approximately 30 days, but the participant-level outcome is the recommendation made at Day 0.
Measured: At the index LDCT report (Day 0)
Secondary outcomes (8)
Proportion of Participants With Under-Management
Percentage of evaluable participants whose final physician recommendation is less intensive than the minimum acceptable management level determined by the blinded independent expert endpoint committee.
Measured: At the index LDCT report (Day 0)
Proportion of Expert-Defined Referral Cases Missed by the Final Report
Among participants whom the blinded independent expert endpoint committee determines require specialist evaluation/referral, the percentage whose final physician report does not recommend specialist evaluation/referral. The denominator includes only expert-defined referral cases.
Measured: At the index LDCT report (Day 0)
Proportion of Participants With Inappropriate Management Escalation
Percentage of evaluable participants whose final physician recommendation is more intensive than the maximum acceptable management level determined by the blinded independent expert endpoint committee.
Measured: At the index LDCT report (Day 0)
Change in Decision Appropriateness After AI Review in the AI-Assisted Arm
In the AI-assisted arm, the physician's locked pre-AI decision will be compared with the final post-AI decision against the same expert-accepted management range. Results will include: (1) correction rate, the number changing from inappropriate to appropriate divided by the number with an inappropriate pre-AI decision; (2) AI-induced error rate, the number changing from appropriate to inappropriate divided by the number with an appropriate pre-AI decision; and (3) net correction rate, calculated as (number corrected minus number made incorrect) divided by all evaluable participants in the AI-assisted arm. These denominators must be locked in the statistical analysis plan before database lock.
Measured: At the index LDCT report (Day 0)
Physician Response to the AI Recommendation
Time in minutes from the start of physician image review to final report completion, as recorded by the reporting platform or study system.
Measured: During the index LDCT reporting session (Day 0)
Proportion of AI-Assisted Cases With System Failure
Percentage of eligible cases in the AI-assisted arm for which the AI system does not return a usable output because of processing failure, timeout, technical incompatibility, or an output meeting the prespecified abnormal-output fallback criteria.
Measured: During the index LDCT reporting session (Day 0)
Proportion of Participants With a Pulmonary Nodule-Related Management Action Within 90 Days
Percentage of participants with at least one documented pulmonary nodule-related management action during follow-up, including specialist evaluation, repeat chest CT, contrast-enhanced CT, PET-CT, biopsy, or surgery. Each component will also be summarized separately. Information will be obtained from available routine records, participant-provided records, or ethics-approved telephone follow-up.
Measured: 90 days after the index LDCT examination (allowable window, ±14 days)
Proportion of Participants With a Reporting-Workflow-Related Management or Data Security Adverse Event
Percentage of participants with a prespecified event potentially related to the assigned reporting workflow, including missed or substantially delayed evaluation of a high-risk nodule, an unnecessary invasive procedure, hospitalization or major psychological/economic burden caused by an erroneous recommendation, or a serious data security event. Serious events will also be reported according to applicable ethics and institutional requirements.
Measured: From the index LDCT report through 90 days after the index examination (allowable window, ±14 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2026
- Study start1 Oct 2026 (estimated)
- Primary completion1 Mar 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary NoduleStudies 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
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 2,000 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
- NCT07841600
- Sponsor's protocol ID
- AI-NC-2026-01
- Acronym
- AI-NoduleCare
- Status last verified
- Sep 2026
- First submitted
- 20 Sep 2026
- Last updated
- 25 Sep 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Utility of Artificial Intelligence-Assisted Management Decisions for Pulmonary Nodules Detected by Low-Dose Computed Tomography in Health Screening: A Multicenter, Prospective, Cluster-Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07841600
- Acronym / sponsor ID
- AI-NoduleCare / AI-NC-2026-01
- Conditions
- Pulmonary Nodule, Clinical Decision Support
- Keywords
- Artificial Intelligence, Low-Dose Computed Tomography, Pulmonary Nodule Management, Lung Cancer Screening, Human-AI Collaboration
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Guangzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Oct 2026 (estimated)
- Primary completion
- 1 Mar 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 25 Sep 2026 (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.
Pulmonary nodules are frequently found during low-dose computed tomography (LDCT) health screening. The main challenge is not only detecting nodules, but also recommending the appropriate next step, such as routine follow-up, short-interval imaging follow-up, or specialist evaluation. This multicenter cluster-randomized trial will evaluate whether an artificial intelligence (AI)-assisted reporting workflow improves the appropriateness of pulmonary nodule management decisions in health examination settings without increasing under-management or missed referrals. Participating health examination branches, rather than individual participants, will be randomly assigned in a 1:1 ratio to a conventional reporting workflow or an AI-assisted reporting workflow. All final reports will be reviewed and signed by qualified physicians. An independent expert endpoint committee, blinded to study assignment and AI output, will determine the acceptable management range for each case. Approximately 2,000 adults with pulmonary nodules detected on LDCT will be included across at branches.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Pulmonary Nodule Reporting Workflow | Experimental | Other: AI-Assisted Pulmonary Nodule Reporting WorkflowPhysicians first record and lock an initial pulmonary nodule management decision without viewing AI results. They then review locked-version AI outputs, including nodule characteristics, estimated malignancy risk, and a management recommendation, and issue the final physician-signed report. Physicians may accept, modify, or reject the AI recommendation. The AI cannot automatically sign reports or directly instruct participants. |
| Conventional Pulmonary Nodule Reporting Workflow | Active comparator | Other: Conventional Pulmonary Nodule Reporting WorkflowPhysicians interpret LDCT examinations and issue pulmonary nodule management recommendations using the participating branch's conventional clinical reporting workflow. Study AI output is not displayed. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Age 18 years or older.
- Undergoing chest low-dose computed tomography at a participating health examination branch during the study recruitment period.
- At least one pulmonary nodule is identified on the index LDCT and requires risk stratification and a management decision regarding follow-up, repeat imaging, or specialist evaluation.
- Thin-section reconstructed images are of sufficient quality for clinical interpretation and, where applicable, AI analysis.
- Required clinical risk information is available, including age, sex, smoking history, history of malignancy, family history of lung cancer, and available prior chest imaging.
Key exclusion criteria (7 listed)
- Previously diagnosed lung cancer or currently receiving lung cancer-related treatment.
- Known pulmonary metastasis from another malignancy.
- Imaging findings that clearly require immediate entry into a lung cancer specialty diagnostic or treatment pathway and are not appropriate for routine pulmonary nodule risk-stratified management.
- An urgent thoracic condition requiring immediate management, such as pneumothorax, large pleural effusion, or acute pulmonary embolism.
- Severe imaging artifact, incompatible slice thickness or reconstruction, or a lesion type, imaging parameter, or disease extent outside the prespecified locked scope of the AI system.
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 |
|---|---|---|
| AI-Assisted Pulmonary Nodule Reporting WorkflowA locked-version artificial intelligence decision-support workflow applied after the physician records an initial assessment. The system displays pulmonary nodule location, size, density, morphologic features, estimated malignancy risk, and a suggested management category. The physician retains final responsibility. If the system fails, times out, or produces an abnormal output, the case returns to manual reporting. No online learning or automatic model updating is allowed during the trial. | Other | AI-Assisted Pulmonary Nodule Reporting Workflow |
| Conventional Pulmonary Nodule Reporting WorkflowStandard manual LDCT interpretation and pulmonary nodule management recommendation by qualified physicians using the participating branch's routine reporting process, without access to study AI output. | Other | Conventional Pulmonary Nodule Reporting Workflow |
Primary outcome
Proportion of Participants With an Appropriate Final Management Recommendation
Time frame: At the index LDCT report (Day 0)
Percentage of evaluable participants whose final physician recommendation is within the acceptable management range determined by the blinded independent expert endpoint committee. Recommendations are classified as routine or annual follow-up, short-interval imaging follow-up, or specialist evaluation/referral. The expert committee will complete adjudication using index data within approximately 30 days, but the participant-level outcome is the recommendation made at Day 0.
8 secondary outcomes
Proportion of Participants With Under-Management
Time frame: At the index LDCT report (Day 0)
Proportion of Expert-Defined Referral Cases Missed by the Final Report
Time frame: At the index LDCT report (Day 0)
Proportion of Participants With Inappropriate Management Escalation
Time frame: At the index LDCT report (Day 0)
Change in Decision Appropriateness After AI Review in the AI-Assisted Arm
Time frame: At the index LDCT report (Day 0)
Physician Response to the AI Recommendation
Time frame: During the index LDCT reporting session (Day 0)
Proportion of AI-Assisted Cases With System Failure
Time frame: During the index LDCT reporting session (Day 0)
Proportion of Participants With a Pulmonary Nodule-Related Management Action Within 90 Days
Time frame: 90 days after the index LDCT examination (allowable window, ±14 days)
Proportion of Participants With a Reporting-Workflow-Related Management or Data Security Adverse Event
Time frame: From the index LDCT report through 90 days after the index examination (allowable window, ±14 days)
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 25 Sep 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Assisted Pulmonary Nodule Reporting Workflow and Conventional Pulmonary Nodule Reporting Workflow in people with Pulmonary Nodule and Clinical Decision Support, run by The First Affiliated Hospital of Guangzhou Medical University.
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 6 inclusion and 7 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: opening soon, as of the sponsor's last update on 25 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion of Participants With an Appropriate Final Management Recommendation (time frame: At the index LDCT report (Day 0)). 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 25 Sep 2026.