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Prospective Assessment of Alignment Between Multimodal Artificial Intelligence and Multidisciplinary Team Decisions in Gastrointestinal Oncology
Sponsor: Shanghai Minimally Invasive Surgery Center
Summary
This prospective, single-center observational study will evaluate the concordance between a multimodal artificial intelligence system and multidisciplinary team decisions in patients with gastrointestinal tumors. For each enrolled case, the AI system and the MDT will independently review the same available clinical information, including medical history, laboratory findings, imaging, pathology, and other relevant diagnostic data, and will generate recommendations regarding diagnosis, staging, treatment planning, and further examinations. An independent expert panel will assess the agreement, clinical appropriateness, and potential major errors of the two decision pathways. AI-generated recommendations will be used solely for research evaluation and will not directly influence patient care. The study aims to determine the feasibility, reliability, and safety of multimodal AI-assisted decision-making in real-world gastrointestinal oncology workflows.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
69
Start Date
2026-08-05
Completion Date
2028-08-05
Last Updated
2026-08-05
Healthy Volunteers
No
Interventions
Multidisciplinary Team Clinical Decision Assessment
Using the same clinical information available at the predefined decision time point, the institutional multidisciplinary team will independently formulate recommendations regarding diagnosis, staging, additional examinations, and treatment planning according to routine clinical practice. Clinical management will remain under the responsibility of the treating physicians and the multidisciplinary team. The MDT decision and the independently generated AI output will subsequently be compared and evaluated by an independent expert panel.
Multimodal Artificial Intelligence-Based Clinical Decision Assessment
Available clinical information for each enrolled participant, including medical history, laboratory findings, endoscopic findings, imaging, pathology, and molecular testing results when available, will be entered into a multimodal artificial intelligence system. The system will independently generate a structured assessment of diagnosis, staging, additional diagnostic tasks, and treatment planning. The AI-generated output will be recorded solely for research comparison, will not be disclosed to the treating multidisciplinary team before its decision is finalized, and will not directly influence patient care.
Locations (1)
Ruijin hospital Shanghai Jiaotong University, School of Medicine
Shanghai, Shanghai Municipality, China