Artificial Intelligence in Obstetric Anesthesia for Hypertensive Disorders of Pregnancy: A Narrative Review
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Abstract
Hypertensive disorders of pregnancy, including preeclampsia and eclampsia, represent a significant global maternal health challenge, affecting 2-8% of pregnancies worldwide and contributing to 11-14% of maternal deaths. The complex, multisystemic nature of these conditions, coupled with their unpredictable clinical trajectories, presents substantial challenges for anesthetic management. This narrative review examines the emerging integration of artificial intelligence (AI) technologies in obstetric anesthesia care for hypertensive disorders, exploring current applications, technological foundations, implementation challenges, and future directions. We synthesize evidence demonstrating AI's potential in continuous monitoring, predictive analytics, personalized care delivery, and clinical decision support. While significant barriers to implementation exist-including technological, regulatory, and ethical considerations-the integration of AI into obstetric anesthesia represents a paradigm shift toward precision, predictive, and personalized maternal care. This narrative review synthesizes current evidence to argue that the primary value of AI in this context is not the replacement of clinical judgment, but its augmentation through the synthesis of high-dimensional, time-series data, thereby enhancing anesthesiologist situation awareness and enabling proactive, rather than reactive, management.
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[16] Kong AYH, Liu N, Tan HS, Sia AT, Sng BL. Artificial intelligence in obstetric anaesthesiology. Int J Obstet Anesth. 2025; 61:104288.
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[18] Layton AT. Artificial intelligence and machine learning in preeclampsia. Arterioscler Thromb Vasc Biol. 2025; 45(2):165-71.
[19] Li T, Xu M, Wang Y, Wang Y, Tang H, Duan H, et al. Prediction model of preeclampsia using ML. Front Endocrinol (Lausanne). 2024; 15:1345573.
[20] Wu Y, Shen L, Zhao L, Lin X, Xu M, Tu Z, et al. Noninvasive prediction of preeclampsia using retinal features. NPJ Digit Med. 2025; 8(1):188.
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[29] Adedinsewo DA, Onietan D, Morales-Lara AC, Moideen Sheriff S, Afolabi BB, Kushimo OA, et al. Challenges in AI implementation in low-resource settings. Front Cardiovasc Med. 2025; 12:1516088.
[30] Tadepalli K, Das A, Meena T, Roy S. Bridging gaps in AI adoption for obstetric care. Comput Methods Programs Biomed. 2025; 263:108682.
[31] Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, et al. AI in maternal-fetal medicine. Technol Health Care. 2024; 32(3):1273-87.
[32] Korytnikova E. AI and women's health: innovations and challenges. Adv Clin Med Res. 2023; 4(3):1-6.
[33] Elbiss HM, Abu-Zidan FM. AI in gynecologic and obstetric emergencies. Int J Emerg Med. 2025; 18(1):20.
[34] Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. Future of AI in obstetrics. BMJ Open. 2023; 13(10):e076017.
[35] Mapari SA, Shrivastava D, Dave A, Bedi GN, Gupta A, Sachani P, et al. Role of AI in maternal health. Cureus. 2024; 16(9):e69555.
[36] Owoche PO, Shisanya MS, Mayeku B, Namusonge LN. Role of AI in reducing maternal mortality. PLoS One. 2025; 20(5):e0323533.
[37] Victor A. AI in maternal and child health. PLOS Digit Health. 2025; 4(7):e0000938.
[38] Cedars-Sinai. Artificial intelligence can improve OB-GYN care [Internet]. Available from: https://www.cedars-sinai.org/newsroom/womens-health-month-artificial-intelligence-can-improve-ob-gyn-care/
[39] Vasudevan L, Kibria M, Kucirka L, Shieh K, Wei M, Masoumi S, Balasubramanian S, Victor A, Conklin J, Gurcan M, Stuebe A, Page D Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review. J Med Internet Res 2025;27:e68225.
[40] Figo. From innovation to impact: revolutionising maternal health through femtech [Internet]. Available from: https://www.figo.org/events/figo-webinars/innovation-impact-revolutionising-maternal-health-through-femtech
[41] Nogueira M, Aparício SL, Duarte I, Silvestre M. Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues. J Clin Med. 2025; 14(11):3860.
[42] Özhüner Y, Özhüner E. AI in improving maternal and fetal health. Mediterr Nurs Midwifery. 2025.
[43] Gates Foundation. The scientist using AI to improve maternal health [Internet]. Available from: https://www.gatesfoundation.org/ideas/articles/ai-maternal-health-pakistan
[2] Sun S, Li W, Zhang X, Aziz AU, Zhang N. Trends in global and regional incidence and prevalence of hypertensive disorders in pregnancy (1990–2021): an age-period-cohort analysis. Sci Rep. 2025; 15(1):1513.
[3] Melesse MF, Aynalem GL, Badi MB, Aynalem BY. Maternal outcomes of severe preeclampsia and eclampsia and associated factors among women admitted at referral hospitals of Amhara regional state, Ethiopia. Front Glob Womens Health. 2025; 6:1555778.
[4] Wojtowicz A, Zembala-Szczerba M, Babczyk D, Kołodziejczyk-Pietruszka M, Lewaczyńska O, Huras H. Early- and late-onset preeclampsia according to new ISSHP criteria. Int J Hypertens. 2019; 2019:4108271.
[5] Valensise H, Vasapollo B, Gagliardi G, Novelli GP. Early and late preeclampsia: two different maternal hemodynamic states. Hypertension. 2008; 52(5):873-80.
[6] Lamarca B. Endothelial dysfunction: an important mediator in the pathophysiology of hypertension during pre-eclampsia. Minerva Ginecol. 2012; 64(4):309-20.
[7] Powe CE, Levine RJ, Karumanchi SA. Preeclampsia: a disease of the maternal endothelium. Circulation. 2011; 123(24):2856-69.
[8] Gyselaers W. Hemodynamic pathways of gestational hypertension and preeclampsia. Am J Obstet Gynecol. 2022; 226(2 Suppl):S988-S1005.
[9] Lees C, Ferrazzi E. Relevance of haemodynamics in treating pre-eclampsia. Curr Hypertens Rep. 2017; 19(9):76.
[10] Khalid F, Mahendraker N, Tonismae T. HELLP syndrome. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan–.
[11] Padden MO. HELLP syndrome: recognition and perinatal management. Am Fam Physician. 1999; 60(3):829-36.
[12] Estcourt LJ, Malouf R, Hopewell S, Doree C, Van Veen J. Platelet transfusions before lumbar punctures or epidural anaesthesia. Cochrane Database Syst Rev. 2018; 4(4):CD011980.
[13] Ghadiali T, Nguyen MT, Lee RH, Sasso EB. Atypical eclampsia in a normotensive patient. Int J Gynaecol Obstet. 2024; 164(2):476-81.
[14] MedLink Neurology. Seizures associated with eclampsia [Internet]. Available from: https://www.medlink.com/articles/seizures-associated-with-eclampsia
[15] Bergman L, Hannsberger D, Schell S, Imberg H, Langenegger E, Moodley A, et al. Cerebral infarcts and edema in preeclampsia and eclampsia. Am J Obstet Gynecol. 2025; 232(6):550.e1-550.e14.
[16] Kong AYH, Liu N, Tan HS, Sia AT, Sng BL. Artificial intelligence in obstetric anaesthesiology. Int J Obstet Anesth. 2025; 61:104288.
[17] Ranjbar A, Montazeri F, Ghamsari SR, Mehrnoush V, Roozbeh N, Darsareh F. ML models for predicting preeclampsia: systematic review. BMC Pregnancy Childbirth. 2024; 24(1):6.
[18] Layton AT. Artificial intelligence and machine learning in preeclampsia. Arterioscler Thromb Vasc Biol. 2025; 45(2):165-71.
[19] Li T, Xu M, Wang Y, Wang Y, Tang H, Duan H, et al. Prediction model of preeclampsia using ML. Front Endocrinol (Lausanne). 2024; 15:1345573.
[20] Wu Y, Shen L, Zhao L, Lin X, Xu M, Tu Z, et al. Noninvasive prediction of preeclampsia using retinal features. NPJ Digit Med. 2025; 8(1):188.
[21] World Economic Forum. 7 ways AI is transforming healthcare [Internet]. Available from: https://www.weforum.org/stories/2025/08/ai-transforming-global-health/
[22] Oppong SO, Twum F, Hayfron-Acquah JB, Missah YM. Computer vision model for medicinal plant identification. Comput Intell Neurosci. 2022; 2022:1189509.
[23] Accenture. Transforming healthcare with AI [Internet]. Available from: https://www.accenture.com/us-en/blogs/health/accenture-technology-trends-2025-healthcare
[24] Health care technology trends 2025 [Internet]. YouTube. Available from: https://www.youtube.com/watch?v=8OWdxCJcQVE
[25] SS&C Blue Prism. The future of AI in healthcare – 2025 [Internet]. Available from: https://www.blueprism.com/resources/blog/the-future-of-ai-in-healthcare/
[26] Canadian Agency for Drugs and Technologies in Health. 2025 watch list: artificial intelligence in health care [Internet]. Ottawa (ON): CADTH; 2025 Mar. Report No.: ER0015.
[27] Stanford HAI. The 2025 AI Index Report [Internet]. Available from: https://hai.stanford.edu/ai-index/2025-ai-index-report
[28] Cicek V, Bagci U. Position of AI in healthcare and future perspective. Artif Intell Med. 2025; 167:103193.
[29] Adedinsewo DA, Onietan D, Morales-Lara AC, Moideen Sheriff S, Afolabi BB, Kushimo OA, et al. Challenges in AI implementation in low-resource settings. Front Cardiovasc Med. 2025; 12:1516088.
[30] Tadepalli K, Das A, Meena T, Roy S. Bridging gaps in AI adoption for obstetric care. Comput Methods Programs Biomed. 2025; 263:108682.
[31] Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, et al. AI in maternal-fetal medicine. Technol Health Care. 2024; 32(3):1273-87.
[32] Korytnikova E. AI and women's health: innovations and challenges. Adv Clin Med Res. 2023; 4(3):1-6.
[33] Elbiss HM, Abu-Zidan FM. AI in gynecologic and obstetric emergencies. Int J Emerg Med. 2025; 18(1):20.
[34] Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. Future of AI in obstetrics. BMJ Open. 2023; 13(10):e076017.
[35] Mapari SA, Shrivastava D, Dave A, Bedi GN, Gupta A, Sachani P, et al. Role of AI in maternal health. Cureus. 2024; 16(9):e69555.
[36] Owoche PO, Shisanya MS, Mayeku B, Namusonge LN. Role of AI in reducing maternal mortality. PLoS One. 2025; 20(5):e0323533.
[37] Victor A. AI in maternal and child health. PLOS Digit Health. 2025; 4(7):e0000938.
[38] Cedars-Sinai. Artificial intelligence can improve OB-GYN care [Internet]. Available from: https://www.cedars-sinai.org/newsroom/womens-health-month-artificial-intelligence-can-improve-ob-gyn-care/
[39] Vasudevan L, Kibria M, Kucirka L, Shieh K, Wei M, Masoumi S, Balasubramanian S, Victor A, Conklin J, Gurcan M, Stuebe A, Page D Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review. J Med Internet Res 2025;27:e68225.
[40] Figo. From innovation to impact: revolutionising maternal health through femtech [Internet]. Available from: https://www.figo.org/events/figo-webinars/innovation-impact-revolutionising-maternal-health-through-femtech
[41] Nogueira M, Aparício SL, Duarte I, Silvestre M. Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues. J Clin Med. 2025; 14(11):3860.
[42] Özhüner Y, Özhüner E. AI in improving maternal and fetal health. Mediterr Nurs Midwifery. 2025.
[43] Gates Foundation. The scientist using AI to improve maternal health [Internet]. Available from: https://www.gatesfoundation.org/ideas/articles/ai-maternal-health-pakistan
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| Pregnancy-Induced Hypertension Preeclampsia Artificial Intelligence Obstetric Anesthesia. | ||
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How to Cite
1.
Abtahi D, Heshmatkhah E. Artificial Intelligence in Obstetric Anesthesia for Hypertensive Disorders of Pregnancy: A Narrative Review. Arch Anesth & Crit Care. 2026;.
