Clinical trial · Interventional
Multi-agent LLMs for Decision Support in Cervical Cancer During Pregnancy
Multi-agent Large Language Models for Multidisciplinary Decision Support in Cervical Cancer During Pregnancy
NCT07318701CI-TRIAL-00100199not yet recruitingN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cervical Cancer | Malignant Cervical Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (4)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| junor doctor group | Other | — | UNRESOLVED |
| junor doctor group with aid of MDT agents | Other | — | UNRESOLVED |
| multi-disciplinary agents group | Other | — | UNRESOLVED |
| real MDT group | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- Arm1: multi-disciplinary agents group
- description
- generate diagnosis and treatment opinions from multi-disciplinary agents
- interventionNames
- Other: multi-disciplinary agents group
- type
- PLACEBO_COMPARATOR
- label
- Arm2: real MDT group/ junior doctors group/junior doctors after referring to agent results group
- description
- generate diagnosis and treatment opinions from real MDT group/ junior doctors group/junior doctors after referring to agent results group
- interventionNames
- Other: real MDT group
- Other: junor doctor group
- Other: junor doctor group with aid of MDT agents
Primary outcomes (1)
- measure
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
Inclusion Criteria: 1. Pathologically confirmed diagnosis of cervical cancer. 2. Confirmed intrauterine pregnancy status via ultrasound. 3. Patients receiving initial treatment. 4. Agreement to participate in the study with signed informed consent. Exclusion Criteria: 1. Previous treatment received for cervical cancer during pregnancy. 2. Pathological pregnancy states (e.g., ectopic pregnancy). 3. Inability or unwillingness to provide signed informed consent.
References
Publications (10)
- RESULTLi R, Wang X, Berlowitz D, Mez J, Lin H, Yu H. CARE-AD: a multi-agent large language model framework for Alzheimer's disease prediction using longitudinal clinical notes. NPJ Digit Med. 2025 Aug 24;8(1):541. doi: 10.1038/s41746-025-01940-4. PMID 40849361
- RESULTMeyer R, Hamilton KM, Truong MD, Wright KN, Siedhoff MT, Brezinov Y, Levin G. ChatGPT compared with Google Search and healthcare institution as sources of postoperative patient instructions after gynecological surgery. BJOG. 2024 Jul;131(8):1154-1156. doi: 10.1111/1471-0528.17746. Epub 2024 Jan 4. No abstract available. PMID 38177090
- RESULTPatel JM, Hermann CE, Growdon WB, Aviki E, Stasenko M. ChatGPT accurately performs genetic counseling for gynecologic cancers. Gynecol Oncol. 2024 Apr;183:115-119. doi: 10.1016/j.ygyno.2024.04.006. Epub 2024 Apr 26. PMID 38676973
- RESULTHermann CE, Patel JM, Boyd L, Growdon WB, Aviki E, Stasenko M. Let's chat about cervical cancer: Assessing the accuracy of ChatGPT responses to cervical cancer questions. Gynecol Oncol. 2023 Dec;179:164-168. doi: 10.1016/j.ygyno.2023.11.008. Epub 2023 Nov 21. PMID 37988948
- RESULTGarg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer. 2023 Nov;1878(6):189026. doi: 10.1016/j.bbcan.2023.189026. Epub 2023 Nov 20. PMID 37980945
- RESULTBedi S, Jain SS, Shah NH. Evaluating the clinical benefits of LLMs. Nat Med. 2024 Sep;30(9):2409-2410. doi: 10.1038/s41591-024-03181-6. No abstract available. PMID 39060659
- RESULTMacchia G, Ferrandina G, Patarnello S, Autorino R, Masciocchi C, Pisapia V, Calvani C, Iacomini C, Cesario A, Boldrini L, Gui B, Rufini V, Gambacorta MA, Scambia G, Valentini V. Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept. Front Oncol. 2022 Jan 3;11:797454. doi: 10.3389/fonc.2021.797454. eCollection 2021.