The aim of this study is to develop an AI-assisted decision-making system based on multi-agent large language models and to evaluate its effectiveness and accuracy in the diagnosis and treatment of cervical cancer during pregnancy.
Pathologically confirmed diagnosis of cervical cancer.
Confirmed intrauterine pregnancy status via ultrasound.
Patients receiving initial treatment.
Agreement to participate in the study with signed informed consent.
Previous treatment received for cervical cancer during pregnancy.
Pathological pregnancy states (e.g., ectopic pregnancy).
Inability or unwillingness to provide signed informed consent.
Parallel
multi-disciplinary agents group
Other interventiongenerate diagnosis and treatment opinions for each case from multi-disciplinary agents
real MDT group
Other interventiongenerate diagnosis and treatment opinions for each case from a real MDT team inclduing senior physicians from relevant departments, including gynecologic oncology, pediatrics, obstetrics, medical oncology and radiation oncology.
junor doctor group
Other interventiongenerate diagnosis and treatment opinions for each case from junior doctor who are residents from relevant departments, including gynecologic oncology, pediatrics, obstetrics, medical oncology and radiation oncology.
junor doctor group with aid of MDT agents
Other interventiongenerate diagnosis and treatment opinions for each case from junior doctor who are residents from relevant departments, including gynecologic oncology, pediatrics, obstetrics, medical oncology and radiation oncology after referring to the results from MDT agent .
Accuracy of the MDT decision
scores, from 0 to 100, accuracy of the MDT decision according to evaluation indicators of each discipline
Consuming time
seconds, consuming time for generating MDT decisions from MDT agents/ real MDT team
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