How do the three most widely accessible large language models perform in terms of accuracy, consistency, and reliability when answering clinically relevant questions derived from the 2022 ESHRE guideline on …
Endometriosis is one of the most common causes of chronic pelvic or lower-abdominal pain, yet mechanisms, predictors, and patient-centered treatment pathways remain insufficiently defined. This article summarizes the situation in …
Endometriosis is a chronic inflammatory condition that can result in chronic pain through complex nociceptive, neuropathic, and nociplastic pathways. This article provides a comprehensive background on endometriosis as well as …
Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion …
Infertility represents a significant global health burden, necessitating advanced therapeutic interventions. While Assisted Reproductive Technologies have revolutionized fertility treatment, they remain constrained by limited efficacy, off-target toxicity, and procedural complexity. …
The appendix is involved in a diverse spectrum of inflammatory, infectious, benign, and malignant conditions that extend far beyond acute appendicitis. Although acute appendicitis remains the most common appendiceal emergency, …
Endometriosis is a chronic, debilitating condition affecting approximately 10-15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial …
Endometriosis is a prevalent chronic inflammatory gynecological disorder affecting approximately 10% of reproductive-age women worldwide, characterized by endometrial-like tissue outside the uterine cavity. Ectopic lesion growth tracks closely with immune-inflammatory …
To develop and internally validate machine-learning models for non-invasive triage of women at risk for endometriosis using structured clinical variables in a laparoscopically and histologically verified cohort.
To compare the quality of AI-generated responses to gynecologic post-operative questions with educational materials published by professional societies.