Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Treat Regimen.
- Who it may be relevant to
- Registry conditions: Nonsmall Cell Lung Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC: a Prospective, Controlled Clinical Trial Protocol
Overview
The goal of this clinical trial is to evaluate the effectiveness and safety of a locally deployed artificial intelligence (AI) decision-support model in the multidisciplinary team (MDT) process for patients with non-small cell lung cancer (NSCLC). The main questions it aims to answer : What is the level of agreement between treatment recommendations generated by the AI model and those made by a traditional MDT? How often do clinicians modify their final treatment decision after reviewing the AI model's recommendation? Researchers will compare treatment plans from the traditional MDT (Arm 1), the AI model (Arm 2), and the clinician's final decision after reviewing the AI output (Arm 3) to assess consistency, decision modification rates, and clinical efficiency. Participants will: Have their clinical, imaging, and molecular data submitted to both the traditional MDT and the AI model for independent treatment recommendations Receive a final treatment plan determined by clinicians after reviewing both recommendations, with follow-up for safety and survival outcomes
Interventions
- Diagnostic test Treat Regimen
The impact of artificial intelligence on clinicians' treatment plans
Primary outcome measures
- Consistency rate [Time frame: Baseline(MDT 1 Day)]
Secondary outcome measures (12)
- MDT Discussion Process Time [Time frame: Baseline(MDT Day 1)]
- Quality of AI Recommendations [Time frame: Baseline(MDT Day 1)]
- Clinical Acceptability of AI [Time frame: Baseline(MDT Day 1)]
- MDT Discussion Efficiency [Time frame: Baseline(MDT Day 1)]
- Process Convenience [Time frame: Baseline(MDT Day 1)]
- Added Value to Clinical Decision [Time frame: Baseline(MDT Day 1)]
- Learning and Training Value [Time frame: Baseline(MDT Day 1)]
- Overall Satisfaction [Time frame: Baseline(MDT Day 1)]
- Willingness to Use in Future [Time frame: Baseline(MDT Day 1)]
- Disease-Free Survival (DFS) [Time frame: 3 years]
- Progression-Free Survival (PFS) [Time frame: 3 years]
- Overall Survival (OS) [Time frame: 3 years]
Eligibility criteria
Inclusion criteria
- Age ≥ 18 years;
- MDT (Multidisciplinary Team) discussion deems a systemic treatment plan necessary;
- Complete clinical, imaging, and molecular pathological data.
Exclusion criteria
- Stage I patients;
- Diagnosed with a thoracic tumor other than NSCLC;
- Lack of detailed medical data, or missing data;
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Treatment
Study locations
China · 1 center
- Guangdong Provincial People's Hospital — Guangzhou
Publications
- Pillay B, Wootten AC, Crowe H, Corcoran N, Tran B, Bowden P, Crowe J, Costello AJ. The impact of multidisciplinary team meetings on patient assessment, management and outcomes in oncology settings: A systematic review of the literature. Cancer Treat Rev. 2016 Jan;42:56-72. doi: 10.1016/j.ctrv.2015.11.007. Epub 2015 Nov 24. PMID 26643552
- Kim JK, Chua ME, Li TG, Rickard M, Lorenzo AJ. Novel AI applications in systematic review: GPT-4 assisted data extraction, analysis, review of bias. BMJ Evid Based Med. 2025 Sep 22;30(5):313-322. doi: 10.1136/bmjebm-2024-113066. PMID 40199559
- Wiegand TLT, Jung LB, Gudera JA, Schuhmacher LS, Moehrle P, Rischewski JF, Mehrzad P, Jeong S, Nguyen LH, Poeschla M, Velezmoro LI, Kruk L, Dimitriadis K, Koerte IK. Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators. NPJ Digit Med. 2025 Jul 19;8(1):459. doi: 10.1038/s41746-025-01817-6. PMID 40683994
Identifiers
NCT: NCT07626736 · KY2025-1003-02