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Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

作者信息

Oliveira Juliana Almeida, Eskandar Karine, Kar Emre, de Oliveira Flávia Ribeiro, Filho Agnaldo Lopes da Silva

机构信息

Department of Women's Health, Federal University of Minas Gerais, Belo Horizonte, Brazil.

Department of Medicine, Pontifical Catholic University of Paraná, Curitiba, Brazil.

出版信息

JMIR AI. 2024 Oct 30;3:e64593. doi: 10.2196/64593.


DOI:10.2196/64593
PMID:39476855
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11561426/
Abstract

BACKGROUND: Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. OBJECTIVE: This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. METHODS: This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. RESULTS: Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. CONCLUSIONS: AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3637/11561426/b196588a0888/ai_v3i1e64593_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3637/11561426/b196588a0888/ai_v3i1e64593_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3637/11561426/b196588a0888/ai_v3i1e64593_fig1.jpg

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本文引用的文献

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Health Care Women Int. 2025

[2]
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[4]
Beyond physical pain: A large-scale cohort study on endometriosis trends and mental health correlates.

J Psychosom Res. 2024-7

[5]
FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial.

PLoS One. 2024

[6]
Accuracy and reproducibility of ChatGPT's free version answers about endometriosis.

Int J Gynaecol Obstet. 2024-5

[7]
Patient experiences of endometriosis diagnosis: A mixed methods approach.

BJOG. 2024-6

[8]
Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe.

BJR Open. 2023-6-30

[9]
Evaluating the performance of ChatGPT in answering questions related to pediatric urology.

J Pediatr Urol. 2024-2

[10]
Users' Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study.

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