{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:31:36Z","timestamp":1782516696505,"version":"3.54.5"},"reference-count":39,"publisher":"Pan American Health Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,9]]},"abstract":"<title>ABSTRACT<\/title>\n<sec>\n<title>Objective.<\/title>\n<p>To assess the effects of the current use of artificial intelligence (AI) in women\u2019s health on health equity, specifically in primary and secondary prevention efforts among women.<\/p>\n<\/sec>\n<sec>\n<title>Methods.<\/title>\n<p>Two databases, Scopus and PubMed, were used to conduct this narrative review. The keywords included \u201cartificial intelligence,\u201d \u201cmachine learning,\u201d \u201cwomen\u2019s health,\u201d \u201cscreen,\u201d \u201crisk factor,\u201d and \u201cprevent,\u201d and papers were filtered only to include those about AI models that general practitioners may use.<\/p>\n<\/sec>\n<sec>\n<title>Results.<\/title>\n<p>Of the 18 articles reviewed, 8 articles focused on risk factor modeling under primary prevention, and 10 articles focused on screening tools under secondary prevention. Gaps were found in the ability of AI models to train using large, diverse datasets that were reflective of the population it is intended for. Lack of these datasets was frequently identified as a limitation in the papers reviewed (<italic>n<\/italic> = 7).<\/p>\n<\/sec>\n<sec>\n<title>Conclusions.<\/title>\n<p>Minority, low-income women have poor access to health care and are, therefore, not well represented in the datasets AI uses to train, which risks introducing bias in its output. To mitigate this, more datasets should be developed to validate AI models, and AI in women\u2019s health should expand to include conditions that affect men and women to provide a gendered lens on these conditions. Public health, medical, and technology entities need to collaborate to regulate the development and use of AI in health care at a standard that reduces bias.<\/p>\n<\/sec>","DOI":"10.26633\/rpsp.2025.19","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:20:49Z","timestamp":1744204849000},"page":"1","source":"Crossref","is-referenced-by-count":2,"title":["AI\u2019s ongoing impact: Implications of AI\u2019s effects on health equity for women\u2019s healthcare providers"],"prefix":"10.26633","volume":"49","author":[{"given":"Suman","family":"Vadlamani","sequence":"first","affiliation":[{"name":"University of Texas Health Science Center at Houston, Houston, TX, United States of America"},{"name":"suman.vadlamani@utsouthwestern.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Wachira","sequence":"additional","affiliation":[{"name":"East Texas A&M University, Commerce, TX, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"11167","reference":[{"key":"ref1","unstructured":"World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: WHO; 2021. Available from: https:\/\/iris.who.int\/\/handle\/10665\/341996."},{"key":"ref2","unstructured":"Wolters Kluwer. Over two-thirds of U.S. physicians have changed their mind, now viewing GenAI as beneficial in healthcare. [Alphen aan den Rijn]: Wolters Kluwer; 2024 Apr 16 [cited 2024 Nov 6]. Available from: https:\/\/www.wolterskluwer.com\/en\/news\/gen-ai-clincian-survey-press-release."},{"key":"ref3","unstructured":"IBM. What is machine learning (ML)?. [Armonk, NY]: IBM; [2024?] [cited 2024 Sept 23]. Available from: https:\/\/www.ibm.com\/topics\/machine-learning."},{"key":"ref4","unstructured":"U.S. Centers for Disease Control and Prevention. Social determinants of health (SDOH) Atlanta, GA: CDC; 2024 [cited 2024 Sept 23]. Available from: https:\/\/www.cdc.gov\/about\/priorities\/why-is-addressing-sdoh-important.html."},{"key":"ref5","unstructured":"American Psychological Association. Fact sheet: Women & socioeconomic status. Washington, D.C.: APA; 2010 [cited 2024 Sept 15]. Available from: https:\/\/www.apa.org\/pi\/ses\/resources\/publications\/women."},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Sukhera J. Narrative reviews: flexible, rigorous, and practical. J Grad Med Educ. 2022;14(4):414\u2013417. https:\/\/doi.org\/10.4300\/JGME-D-22-00480.1.","DOI":"10.4300\/JGME-D-22-00480.1"},{"key":"ref7","unstructured":"United States Preventive Services Task Force. Home page. Rockville, MD: USPSTF; 2017 [cited 2024 Sept 15]. Available from: https:\/\/www.uspreventiveservicestaskforce.org\/uspstf\/."},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Hua A, Quicksall Z, Di C, Motl R, LaCroix AZ, Schatz B, et al. Accelerometer-based predictive models of fall risk in older women: a pilot study. npj Digital Med. 2018;1:25. https:\/\/doi.org\/10.1038\/s41746-018-0033-5.","DOI":"10.1038\/s41746-018-0033-5"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Dehingia N, Dey AK, McDougal L, McAuley J, Singh A, Raj A. Help seeking behavior by women experiencing intimate partner violence in India: A machine learning approach to identifying risk factors. PLoS ONE. 2022;17(2):e0262538. https:\/\/doi.org\/10.1371\/journal.pone.0262538.","DOI":"10.1371\/journal.pone.0262538"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Gastounioti A, Eriksson M, Cohen EA, Mankowski W, Pantalone L, Ehsan S, et al. External validation of a mammography-derived AI-based risk model in a U.S. breast cancer screening cohort of white and black women. Cancers. 2022;14(19):4803. https:\/\/doi.org\/10.3390\/cancers14194803.","DOI":"10.3390\/cancers14194803"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Lilhore UK, Dalal S, Varshney N, Sharma YK, Rao KBVB, Rao VVRM, et al. Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model. Sci Rep. 2024;14(1):4533. https:\/\/doi.org\/10.1038\/s41598-024-54927-8.","DOI":"10.1038\/s41598-024-54927-8"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Wu Q, Dai J. Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score. J Bone Miner Res. 2024;39(4):462\u2013472. https:\/\/doi.org\/10.1002\/jbmr.4663.","DOI":"10.1093\/jbmr\/zjae025"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Zeitlin J, Parides MK, Lane JM, Russell LA, Kunze KN. A clinical prediction model for 10-year risk of self-reported osteoporosis diagnosis in pre- and perimenopausal women. Arch Osteoporos. 2023;18(1):78. https:\/\/doi.org\/10.1007\/s11657-023-01292-0.","DOI":"10.1007\/s11657-023-01292-0"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Trifan A, Semeraro D, Drake J, Bukowski R, Oliveira JL. Social media mining for postpartum depression prediction. Stud Health Technol Inform. 2020;270:1391\u20131392. https:\/\/doi.org\/10.3233\/SHTI200457.","DOI":"10.3233\/SHTI200457"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Akbulut A, Ertugrul E, Topcu V. Fetal health status prediction based on maternal clinical history using machine learning techniques. Comput Methods Programs Biomed. 2018;163:87\u2013100. https:\/\/doi.org\/10.1016\/j.cmpb.2018.06.010.","DOI":"10.1016\/j.cmpb.2018.06.010"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Gao C, Osmundson S, Yan X, Edwards DV, Malin BA, Chen Y. Learning to identify severe maternal morbidity from electronic health records. Stud Health Technol Inform. 2019;264:143\u2013147. https:\/\/doi.org\/10.3233\/SHTI190200.","DOI":"10.3233\/SHTI190200"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Tison GH, Avram R, Nah G, Klein L, Howard BV, Allison MA, et al. Predicting incident heart failure in women with Machine Learning: The Women\u2019s Health Initiative cohort. Can J Cardiol. 2021;37(11):1708\u20131714. https:\/\/doi.org\/10.1016\/j.cjca.2021.08.006.","DOI":"10.1016\/j.cjca.2021.08.006"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Raza A, Siddiqui HUR, Munir K, Almutairi M, Rustam F, Ashraf I. Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction. PLoS ONE. 2022;17(11):e0276525. https:\/\/doi.org\/10.1371\/journal.pone.0276525.","DOI":"10.1371\/journal.pone.0276525"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Lin H, Chen H, Wang X, Wang Q, Wang L, Heng P-A. Dual-path network with synergistic grouping loss and evidence driven risk stratification for whole slide cervical image computation. Med Image Anal. 2021;69:101955. https:\/\/doi.org\/10.1016\/j.media.2021.101955.","DOI":"10.1016\/j.media.2021.101955"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Liu S, Chu R, Xie J, Song K, Su X. Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry. Cytometry A. 2022;103(3):240\u2013250. https:\/\/doi.org\/10.1002\/cyto.a.24684.","DOI":"10.1002\/cyto.a.24684"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Rahman H, Naik Bukht TF, Ahmad R, Almadhor A, Javed AR. Efficient breast cancer diagnosis from complex mammographic images using deep convolutional neural network. Comput Intell Neurosci. 2023;2023:7717712. https:\/\/doi.org\/10.1155\/2023\/7717712.","DOI":"10.1155\/2023\/7717712"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Eriksson M, Czene K, Vachon C, Conant EF, Hall P. A clinical risk model for personalized screening and prevention of breast cancer. Cancers (Basel). 2023;15(12):3246. https:\/\/doi.org\/10.3390\/cancers15123246.","DOI":"10.3390\/cancers15123246"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Wei M, Du Y, Wu X, Su Q, Zhu J, Zheng L, et al. A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Comput Math Methods Med. 2020;2020:5894010. https:\/\/doi.org\/10.1155\/2020\/5894010.","DOI":"10.1155\/2020\/5894010"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Zhang X, Lin X, Zhang Z, Dong L, Sun X, Sun D, et al. Artificial intelligence medical ultrasound equipment: application of breast lesions detection. Ultrason Imaging. 2020;42(4\u20135):191\u2013202. https:\/\/doi.org\/10.1177\/0161734620928453.","DOI":"10.1177\/0161734620928453"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Xu Y, Wang Y, Yuan J, Cheng Q, Wang X, Carson PL. Medical breast ultrasound image segmentation by machine learning. Ultrasonics. 2019;91:1\u20139. https:\/\/doi.org\/10.1016\/j.ultras.2018.07.006.","DOI":"10.1016\/j.ultras.2018.07.006"},{"key":"ref26","unstructured":"Balch B. Why we know so little about women\u2019s health. Washington, D.C.: Association of American Medical Colleges; 2024 [cited 2024 Sept 15]. Available from: https:\/\/www.aamc.org\/news\/why-we-know-so-little-about-women-s-health."},{"key":"ref27","unstructured":"U.S. Centers for Disease Control and Prevention. About Women and Heart Disease. Atlanta, GA: CDC; 2024 [cited 2024 Sept 15]. Available from: https:\/\/www.cdc.gov\/heart-disease\/about\/women-and-heart-disease.html."},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Sutton MY, Anachebe NF, Lee R, Skanes H. Racial and ethnic disparities in reproductive health services and outcomes, 2020. Obstet Gynecol. 2021;137(2):225\u2013233. https:\/\/doi.org\/10.1097\/aog.0000000000004224.","DOI":"10.1097\/AOG.0000000000004224"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Chinn JJ, Martin IK, Redmond N. Health equity among black women in the United States. J Womens Health (Larchmt). 2021;30(2):212\u2013219. https:\/\/doi.org\/10.1089\/jwh.2020.8868.","DOI":"10.1089\/jwh.2020.8868"},{"key":"ref30","unstructured":"U.S. Department of Health and Human Services. Heart Disease and African Americans. Rockville, MD: HHS; 2023 [cited 2024 Sept 15]. Available from: https:\/\/minorityhealth.hhs.gov\/heart-disease-and-african-americans."},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Dreyfus B, Kuri L, Ferri M, Doykos P, Fazeli MS, Hofer K, et al. Understanding Hispanic\/Latino participation in clinical trials and observational studies, and strategies to increase participation: a targeted literature review. J Health Care Poor Underserved. 2023;34(1):399\u2013424. https:\/\/doi.org\/10.1353\/hpu.2023.0026.","DOI":"10.1353\/hpu.2023.0026"},{"key":"ref32","unstructured":"Ramjewan A. Exploring the future of artificial intelligence in cardiovascular medicine: A discussion with Mayo Clinic\u2019s Dr. Demilade Adedinsewo. Rochester, MN: Mayo Foundation for Medical Education and Research; 2022 [cited 2024 Sept 15]. Available from: https:\/\/innovationexchange.mayoclinic.org\/future-of-artificial-intelligence-in-cardiovascular-medicine-with-mayo-clinics-dr-demilade-adedinsewo\/."},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"McCarthy AM, Keller BM, Pantalone LM, Hsieh MK, Synnestvedt M, Conant EF, et al. Racial differences in quantitative measures of area and volumetric breast density. J Natl Cancer Inst. 2016;108(10):djw104. https:\/\/doi.org\/10.1093\/jnci\/djw104.","DOI":"10.1093\/jnci\/djw104"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A systematic review of the barriers to the implementation of artificial intelligence in healthcare. Cureus. 2023;15(10):e46454. https:\/\/doi.org\/10.7759\/cureus.46454.","DOI":"10.7759\/cureus.46454"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Khanna NN, Maindarkar MA, Viswanathan V, Fernandes JFE, Paul S, Bhagawati M, et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare (Basel). 2022;10(12):2493. https:\/\/doi.org\/10.3390\/healthcare10122493.","DOI":"10.3390\/healthcare10122493"},{"key":"ref36","unstructured":"Kasyanau A. Balancing the cost of AI in healthcare: future savings vs current spending. [Jersey City, NJ]: Forbes; 2024 [cited 2024 Nov 7]. Available from: https:\/\/www.forbes.com\/councils\/forbestechcouncil\/2024\/04\/17\/balancing-the-cost-of-ai-in-healthcare-future-savings-vs-current-spending\/."},{"key":"ref37","unstructured":"U.S. Department of Health and Human Services. Hispanic\/Latino Health. Rockville, MD: HHS; 2024 [cited 2024 Sept 15]. Available from: https:\/\/minorityhealth.hhs.gov\/hispaniclatino-health."},{"key":"ref38","unstructured":"National Heart, Lung, and Blood Institute, National Institutes of Health. Women\u2019s Health Initiative (WHI). Bethesda, MD: NHLBI; [date unknown] [cited 2024 Sept 15]. Available from: https:\/\/www.nhlbi.nih.gov\/science\/womens-health-initiative-whi."},{"key":"ref39","unstructured":"Skloot R. The immortal life of Henrietta Lacks. New York: Crown Publishing Group; 2010."}],"container-title":["Revista Panamericana de Salud P\u00fablica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/iris.paho.org\/bitstream\/handle\/10665.2\/65979\/v49192025.pdf?sequence=1&isAllowed=y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:21:32Z","timestamp":1744204892000},"score":1,"resource":{"primary":{"URL":"https:\/\/iris.paho.org\/handle\/10665.2\/65979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":39,"alternative-id":["RPSP.2025.19"],"URL":"https:\/\/doi.org\/10.26633\/rpsp.2025.19","relation":{},"ISSN":["1020-4989","1680-5348"],"issn-type":[{"value":"1020-4989","type":"print"},{"value":"1680-5348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]}}}