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Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and

Yanhong Wang^1,2, Jialiang Yao^1,2, Jianhui Tian^1,2

¹Clinical Oncology Center, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, No. 274 Zhijiang Middle Road, Jing'an District, Shanghai, 200071, China, 86 13761351319.

JMIR Research Protocols

|July 10, 2026

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This systematic review evaluates large language models (LLMs) in ovarian cancer (OC) care. Findings will guide evidence-based integration of AI tools, addressing performance and safety challenges in gynecologic oncology.

Area of Science:

Gynecologic Oncology
Artificial Intelligence
Medical Informatics

Background:

Ovarian cancer (OC) presents significant management challenges and poor survival rates for advanced stages.
Large language models (LLMs) are emerging AI tools with potential applications in healthcare, including diagnostic support and treatment planning.
The application of LLMs in ovarian cancer care remains largely unassessed.

Purpose of the Study:

To systematically review and meta-analyze the use, performance, and clinical impact of LLMs in ovarian cancer management.
To evaluate LLM applications across diagnostic, prognostic, treatment planning, and patient engagement domains.
To identify metrics for assessing LLM performance and understand their strengths and limitations in OC care.

Main Methods:

Keywords:

AI PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses artificial intelligence large language models meta-analysis ovarian neoplasms systematic review

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Systematic review and meta-analysis adhering to PRISMA-P guidelines.
Comprehensive literature search across biomedical, technical, and Chinese databases until December 31, 2025.
Dual independent review, data extraction, and quality appraisal using validated tools for eligible studies.

Main Results:

The review is ongoing, with literature search and selection in progress; expected completion by mid-2026.
Anticipated results include pooled performance metrics (accuracy, F1-score, AUC) and qualitative insights into clinical integration.
Identification of limitations such as reporting bias and insufficient external validation is expected.

Conclusions:

This review offers the first comprehensive synthesis of LLM applications in ovarian cancer care.
Findings will identify promising use cases, safety concerns, and reporting challenges for AI in gynecologic oncology.
The results aim to inform evidence-based integration of LLMs, promoting transparency and rigor in AI evaluation.