Journal of minimally invasive gynecology · 2026
Artificial Intelligence in Minimally Invasive Gynecological Surgery: A Systematic Review of Task-Specific Performance and Clinical Translational Readiness.
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Alnajjar Eman, Hajeri Fatma Al, Alsaad Latifa, Harrity Conor, Chummun Kushal, Rajab Hassan
Аннотация
OBJECTIVE: To systematically evaluate the task-specific performance and clinical translational readiness of artificial intelligence (AI) applications across the preoperative, intraoperative, and postoperative phases of minimally invasive gynecologic surgery (MIGS).
DATA SOURCES: A comprehensive search of PubMed, Embase, Scopus, Web of Science, Google Scholar, CENTRAL, and the Cochrane Library was conducted for English-language studies published between January 1, 2015, and August 31, 2025.
METHODS OF STUDY SELECTION: This systematic review followed PRISMA guidelines. Studies evaluating AI or machine learning models for predictive or diagnostic tasks in MIGS (laparoscopy, hysteroscopy, or robotic surgery) were included. Animal studies, technical feasibility reports without clinical outcomes, and non-English publications were excluded. Risk of bias was assessed using a validated tool for AI predicition models (PROBAST-AI) framework.
TABULATION, INTEGRATION, AND RESULTS: Eight studies involving 6577 patients met the inclusion criteria. AI applications were categorized according to the perioperative phase. Preoperative models for endometriosis and adnexal torsion demonstrated high sensitivity (95%-96%) but limited specificity (11%-45%), suggesting utility as screening triage rather than definitive diagnostic systems. One randomized controlled trial demonstrated that AI-assisted surgical planning for hysteroscopic myomectomy significantly reduced operative time and intraoperative blood loss (p <.05). Postoperative deep learning models predicting fertility outcomes following hysteroscopic adhesiolysis demonstrated the highest discriminative performance (area under curve >0.95) and were the only studies incorporating prospective external validation. PROBAST-AI assessment identified high risk of bias in 75% of studies.
CONCLUSION: AI applications in MIGS demonstrate promising task-specific performance; however, evidence is predominantly observational with high risk of bias and limited external validation, constraining clinical translation. Multicenter prospective validation is required before routine adoption.
DATA SOURCES: A comprehensive search of PubMed, Embase, Scopus, Web of Science, Google Scholar, CENTRAL, and the Cochrane Library was conducted for English-language studies published between January 1, 2015, and August 31, 2025.
METHODS OF STUDY SELECTION: This systematic review followed PRISMA guidelines. Studies evaluating AI or machine learning models for predictive or diagnostic tasks in MIGS (laparoscopy, hysteroscopy, or robotic surgery) were included. Animal studies, technical feasibility reports without clinical outcomes, and non-English publications were excluded. Risk of bias was assessed using a validated tool for AI predicition models (PROBAST-AI) framework.
TABULATION, INTEGRATION, AND RESULTS: Eight studies involving 6577 patients met the inclusion criteria. AI applications were categorized according to the perioperative phase. Preoperative models for endometriosis and adnexal torsion demonstrated high sensitivity (95%-96%) but limited specificity (11%-45%), suggesting utility as screening triage rather than definitive diagnostic systems. One randomized controlled trial demonstrated that AI-assisted surgical planning for hysteroscopic myomectomy significantly reduced operative time and intraoperative blood loss (p <.05). Postoperative deep learning models predicting fertility outcomes following hysteroscopic adhesiolysis demonstrated the highest discriminative performance (area under curve >0.95) and were the only studies incorporating prospective external validation. PROBAST-AI assessment identified high risk of bias in 75% of studies.
CONCLUSION: AI applications in MIGS demonstrate promising task-specific performance; however, evidence is predominantly observational with high risk of bias and limited external validation, constraining clinical translation. Multicenter prospective validation is required before routine adoption.