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
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
TREATMENT
Masking
NONE
Enrollment
300
The impact of artificial intelligence on clinicians' treatment plans
Guangdong Provincial People's Hospital
Guangzhou, Guangdong, China
RECRUITINGConsistency rate
Consistency rate between Option 1 and Option 2 (calculated using Kappa value). Consistency rate between Option 1 and Option 3 (decision modification rate).
Time frame: Baseline(MDT 1 Day)
MDT Discussion Process Time
Time from start to end of multidisciplinary team (MDT) discussion, measured immediately after MDT end.
Time frame: Baseline(MDT Day 1)
Quality of AI Recommendations
Physician-rated quality of AI recommendations using a Likert 5-point scale (1 = very poor, 5 = excellent).
Time frame: Baseline(MDT Day 1)
Clinical Acceptability of AI
Physician-rated clinical acceptability of AI recommendations using a Likert 5-point scale (1 = unacceptable, 5 = fully acceptable).
Time frame: Baseline(MDT Day 1)
MDT Discussion Efficiency
Physician-rated efficiency of MDT discussion process aided by AI using a Likert 5-point scale (1 = very inefficient, 5 = very efficient).
Time frame: Baseline(MDT Day 1)
Process Convenience
Physician-rated convenience of the AI-integrated workflow using a Likert 5-point scale (1 = very inconvenient, 5 = very convenient).
Time frame: Baseline(MDT Day 1)
Added Value to Clinical Decision
Physician-rated added value of AI to clinical decision-making using a Likert 5-point scale (1 = no added value, 5 = significant added value).
Time frame: Baseline(MDT Day 1)
Learning and Training Value
Physician-rated learning and training value of AI system using a Likert 5-point scale (1 = no value, 5 = high value).
Time frame: Baseline(MDT Day 1)
Overall Satisfaction
Physician-rated overall satisfaction with AI-assisted MDT using a Likert 5-point scale (1 = very dissatisfied, 5 = very satisfied).
Time frame: Baseline(MDT Day 1)
Willingness to Use in Future
Physician-rated willingness to use AI system in future clinical practice using a Likert 5-point scale (1 = definitely not willing, 5 = definitely willing).
Time frame: Baseline(MDT Day 1)
Disease-Free Survival (DFS)
Time from treatment initiation to disease recurrence or death from any cause, assessed every 3-6 months during 2-3 years follow-up.
Time frame: 3 years
Progression-Free Survival (PFS)
Time from treatment initiation to disease progression or death from any cause, assessed every 3-6 months during 2-3 years follow-up.
Time frame: 3 years
Overall Survival (OS)
Time from treatment initiation to death from any cause, assessed every 3-6 months during 2-3 years follow-up.
Time frame: 3 years
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