Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things

Abstract

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing health care delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven clinical decision support systems (CDSSs) can predict glucose trends and provide tailored evidence-based treatments with real-time adjustments as insulin resistance changes with placental growth. Additionally, mobile health (mHealth) applications facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient–provider relationship.

Key Points

• The IoMT expands how patients interact with their health care.

• AI has widespread application in the care of pregnancies complicated by diabetes.

• A need for validation and black-box methodologies challenges the application of AI-based tools.

• As research in AI grows, considerations for data privacy and ethical dilemmas will be required.

Keywords

artificial intelligence - internet of Medical Things - diabetes - gestational diabetes - pregnancy - remote patient monitoring

Publication History

Received: 29 June 2024

Accepted: 25 November 2024

Accepted Manuscript online:
26 November 2024

Article published online:
24 December 2024

• References

• 1 McMicking J, Lam AYR, Lim W, Tan L-K. Epidemiology and classification of diabetes in pregnancy. In: Maternal Medical Health Disorders in Pregnancy. 2021
• 2 American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care 2018; 41 (05) 917-928
• 3 Malaza N, Masete M, Adam S, Dias S, Nyawo T, Pheiffer C. A systematic review to compare adverse pregnancy outcomes in women with pregestational diabetes and gestational diabetes. Int J Environ Res Public Health 2022; 19 (17) 10846
• 4 Tornvall I, Kenny D, Wubishet BL, Russell A, Menon A, Comans T. Economic evaluations of mhealth interventions for the management of type 2 diabetes: a scoping review. J Diabetes Sci Technol 2023;19322968231183956
• 5 Chan JCN, Lim LL, Wareham NJ. et al. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet 2021; 396 (10267): 2019-2082
• 6 Güneş Öztürk G, Akyıldız D, Karaçam Z. The impact of telehealth applications on pregnancy outcomes and costs in high-risk pregnancy: a systematic review and meta-analysis. J Telemed Telecare 2024; 30 (04) 607-630
• 7 Ali O, Abdelbaki W, Shrestha A. et al. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl 2023; 8 (01) 100333
• 8 Manickam P, Mariappan SA, Murugesan SM. et al. Artificial intelligence (AI) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors (Basel) 2022; 12 (08) 562
• 9 Hunter DJ, Holmes C. Where medical statistics meets artificial intelligence. N Engl J Med 2023; 389 (13) 1211-1219
• 10 Hadar E, Wolff L, Tenenbaum-Gavish K. et al. Mobile self-operated home ultrasound system for remote fetal assessment during pregnancy. Telemed J E Health 2022; 28 (01) 93-101
• 11 Pokaprakarn T, Prieto JC, Price JT. et al. AI estimation of gestational age from blind ultrasound sweeps in low-resource settings. NEJM Evid 2022; 1 (05)
• 12 Pontones CA, Titzmann A, Huebner H. et al. Feasibility and acceptance of self-guided mobile ultrasound among pregnant women in routine prenatal care. J Clin Med 2023; 12 (13) 4224
• 13 Caballero-Ruiz E, García-Sáez G, Rigla M, Villaplana M, Pons B, Hernando ME. A web-based clinical decision support system for gestational diabetes: automatic diet prescription and detection of insulin needs. Int J Med Inform 2017; 102: 35-49
• 14 Carter J, Sandall J, Shennan AH, Tribe RM. Mobile phone apps for clinical decision support in pregnancy: a scoping review. BMC Med Inform Decis Mak 2019; 19 (01) 219
• 15 Daley BJ, Ni'Man M, Neves MR. et al. mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: a scoping review. Diabet Med 2022; 39 (01) e14735
• 16 Du Y, McNestry C, Wei L, Antoniadi AM, McAuliffe FM, Mooney C. Machine learning-based clinical decision support systems for pregnancy care: a systematic review. Int J Med Inform 2023; 173: 105040
• 17 Nimri R, Battelino T, Laffel LM. et al; NextDREAM Consortium. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med 2020; 26 (09) 1380-1384
• 18 Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med 2020; 3: 126
• 19 Broome DT, Hilton CB, Mehta N. Policy implications of artificial intelligence and machine learning in diabetes management. Curr Diab Rep 2020; 20 (02) 5
• 20 Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69S: S36-S40
• 21 American College of Obstetricians and Gynecologists. Implementing telehealth in practice. Obstet Gynecol 2020; 135 (02) e73-e79
• 22 Zork NM. Telehealth for the management of diabetes in pregnancy. Curr Diab Rep 2022; 22 (08) 365-369
• 23 Atkinson J, Hastie R, Walker S, Lindquist A, Tong S. Telehealth in antenatal care: recent insights and advances. BMC Med 2023; 21 (01) 332
• 24 Ghimire S, Martinez S, Hartvigsen G, Gerdes M. Virtual prenatal care: a systematic review of pregnant women's and healthcare professionals' experiences, needs, and preferences for quality care. Int J Med Inform 2023; 170: 104964
• 25 Thomas Craig KJ, Willis VC, Gruen D, Rhee K, Jackson GP. The burden of the digital environment: a systematic review on organization-directed workplace interventions to mitigate physician burnout. J Am Med Inform Assoc 2021; 28 (05) 985-997
• 26 Gichoya JW, Thomas K, Celi LA. et al. AI pitfalls and what not to do: mitigating bias in AI. Br J Radiol 2023; 96 (1150) 20230023
• 27 Moulaei K, Yadegari A, Baharestani M, Farzanbakhsh S, Sabet B, Reza Afrash M. Generative artificial intelligence in healthcare: a scoping review on benefits, challenges and applications. Int J Med Inform 2024; 188: 105474
• 28 Mennickent D, Rodríguez A, Opazo MC. et al. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14: 1130139
• 29 Kebede MM, Pischke CR. Popular diabetes apps and the impact of diabetes app use on self-care behaviour: a survey among the digital community of persons with diabetes on social media. Front Endocrinol 2019; 10: 135
• 30 Crimmins SD, Ginn-Meadow A, Jessel RH, Rosen JA. Leveraging technology to improve diabetes care in pregnancy. Clin Diabetes 2020; 38 (05) 486-494
• 31 Eberle C, Löhnert M, Stichling S. Effectiveness of disease-specific mhealth apps in patients with diabetes mellitus: scoping review. JMIR Mhealth Uhealth 2021; 9 (02) e23477
• 32 Wang H, Li N, Chivese T. et al; IDF Diabetes Atlas Committee Hyperglycaemia in Pregnancy Special Interest Group. IDF Diabetes Atlas: Estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group's Criteria. Diabetes Res Clin Pract 2022; 183: 109050
• 33 American College of Obstetricians and Gynecologists. Screening for gestational and pregestational diabetes in pregnancy and postpartum. Obstet Gynecol 2024; (e-pub ahead of print)
• 34 Metzger BE, Lowe LP, Dyer AR. et al; HAPO Study Cooperative Research Group. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 2008; 358 (19) 1991-2002
• 35 Gallardo-Rincón H, Ríos-Blancas MJ, Ortega-Montiel J. et al. MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women. Sci Rep 2023; 13 (01) 6992
• 36 Sovio U, Murphy HR, Smith GC. Accelerated fetal growth prior to diagnosis of gestational diabetes mellitus: a prospective cohort study of nulliparous women. Diabetes Care 2016; 39 (06) 982-987
• 37 Zhang C, Rawal S, Chong YS. Risk factors for gestational diabetes: is prevention possible?. Diabetologia 2016; 59 (07) 1385-1390
• 38 Kusinski LC, Brown J, Hughes DJ, Meek CL. Feasibility and acceptability of continuous glucose monitoring in pregnancy for the diagnosis of gestational diabetes: a single-centre prospective mixed methods study. PLoS One 2023; 18 (09) e0292094
• 39 Di Filippo D, Henry A, Bell C. et al. A new continuous glucose monitor for the diagnosis of gestational diabetes mellitus: a pilot study. BMC Pregnancy Childbirth 2023; 23 (01) 186
• 40 Di Filippo D, Darling J, Chang MHY, Henry A, Welsh A. Oral glucose tolerance test and continuous glucose monitoring for gestational diabetes diagnosis: a survey study of women and health care professionals. Arch Gynecol Obstet 2024; 309 (02) 483-489
• 41 Lim BSY, Yang Q, Choolani M. et al. Utilizing continuous glucose monitoring for early detection of gestational diabetes mellitus and pregnancy outcomes in an Asian population. Diabetes Care 2024; 47 (11) 1916-1921
• 42 Fishel Bartal M, Ashby Cornthwaite J, Ghafir D. et al. Continuous glucose monitoring in individuals undergoing gestational diabetes screening. Am J Obstet Gynecol 2023; 229 (04) 441.e1-441.e14
• 43 Scifres CM, Lowe WL. Continuous glucose monitoring in pregnancy: new insights into gestational diabetes with more to learn. Diabetes Care 2024; 47 (08) 1319-1321
• 44 Bellemo V, Lim ZW, Lim G. et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digit Health 2019; 1 (01) e35-e44
• 45 Wolf RM, Channa R, Liu TYA. et al. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun 2024; 15 (01) 421
• 46 Lee AY, Yanagihara RT, Lee CS. et al. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care 2021; 44 (05) 1168-1175
• 47 Yang Z, Tan T-E, Shao Y, Wong TY, Li X. Classification of diabetic retinopathy: past, present and future. Front Endocrinol (Lausanne) 2022; 13: 1079217
• 48 Maddela VS. AEYE Health gets FDA nod for AI diabetic retinopathy screening tool. NS Medical Devices. May 1, 2024
• 49 Stringer JSA, Pokaprakarn T, Prieto JC. et al. Diagnostic accuracy of an integrated AI tool to estimate gestational age from blind ultrasound sweeps. JAMA 2024; 332 (08) 649-657
• 50 Nadeau HCG, Maxted ME, Madhavan D, Pierce SL, Feghali M, Scifres C. Insulin dosing, glycemic control, and perinatal outcomes in pregnancies complicated by type-2 diabetes. Am J Perinatol 2021; 38 (06) 535-543
• 51 Hameed AB, Combs CA. Patient Safety and Quality Committee, Society for Maternal-Fetal Medicine. Electronic address: [e-mail]. Society for Maternal-Fetal Medicine Special Statement: updated checklist for antepartum care of pregestational diabetes mellitus. Am J Obstet Gynecol 2020; 223 (05) B2-B5
• 52 Yoo JH, Kim JH. Advances in continuous glucose monitoring and integrated devices for management of diabetes with insulin-based therapy: improvement in glycemic control. Diabetes Metab J 2023; 47 (01) 27-41
• 53 Galindo RJ, Aleppo G. Continuous glucose monitoring: the achievement of 100 years of innovation in diabetes technology. Diabetes Res Clin Pract 2020; 170: 108502
• 54 Urakami T, Terada H, Yoshida K. et al. Comparison of the clinical effects of intermittently scanned and real-time continuous glucose monitoring in children and adolescents with type 1 diabetes: a retrospective cohort study. J Diabetes Investig 2022; 13 (10) 1745-1752
• 55 American Diabetes Association. (2) Classification and diagnosis of diabetes. Diabetes Care 2015; 38 (Suppl): S8-S16
• 56 Feig DS, Donovan LE, Corcoy R. et al; CONCEPTT Collaborative Group. Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial. Lancet 2017; 390 (10110): 2347-2359
• 57 Petrovski G, Dimitrovski C, Bogoev M, Milenkovic T, Ahmeti I, Bitovska I. Is there a difference in pregnancy and glycemic outcome in patients with type 1 diabetes on insulin pump with constant or intermittent glucose monitoring? A pilot study. Diabetes Technol Ther 2011; 13 (11) 1109-1113
• 58 Battarbee AN, Sauer SM, Sanusi A, Fulcher I. Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes. Am J Obstet Gynecol 2024; 231 (01) 122.e1-122.e9
• 59 Voormolen DN, DeVries JH, Sanson RME. et al. Continuous glucose monitoring during diabetic pregnancy (GlucoMOMS): a multicentre randomized controlled trial. Diabetes Obes Metab 2018; 20 (08) 1894-1902
• 60 Majewska A, Stanirowski PJ, Tatur J. et al. Flash glucose monitoring in gestational diabetes mellitus (FLAMINGO): a randomised controlled trial. Acta Diabetol 2023; 60 (09) 1171-1177
• 61 Berget C, Messer LH, Forlenza GP. A clinical overview of insulin pump therapy for the management of diabetes: past, present, and future of intensive therapy. Diabetes Spectr 2019; 32 (03) 194-204
• 62 Pinsker JE, Bartee A, Katz M. et al. Predictive low-glucose suspend necessitates less carbohydrate supplementation to rescue hypoglycemia: need to revisit current hypoglycemia treatment guidelines. Diabetes Technol Ther 2021; 23 (07) 512-516
• 63 Sora ND, Shashpal F, Bond EA, Jenkins AJ. Insulin pumps: review of technological advancement in diabetes management. Am J Med Sci 2019; 358 (05) 326-331
• 64 Lee TTM, Collett C, Bergford S. et al; AiDAPT Collaborative Group. Automated insulin delivery in women with pregnancy complicated by type 1 diabetes. N Engl J Med 2023; 389 (17) 1566-1578
• 65 Benhalima K, Beunen K, Van Wilder N. et al. Comparing advanced hybrid closed loop therapy and standard insulin therapy in pregnant women with type 1 diabetes (CRISTAL): a parallel-group, open-label, randomised controlled trial. Lancet Diabetes Endocrinol 2024; 12 (06) 390-403
• 66 Nathan DM, Genuth S, Lachin J. et al; Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993; 329 (14) 977-986
• 67 Rickert M, Caughey AB, Valent AM. Medications for managing preexisting and gestational diabetes in pregnancy. Obstet Gynecol Clin North Am 2023; 50 (01) 121-136
• 68 Sherr JL, Heinemann L, Fleming GA. et al. Automated insulin delivery: benefits, challenges, and recommendations. A consensus report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association. Diabetologia 2023; 66 (01) 3-22
• 69 Nally LM, Blanchette JE. Integrated strategies to support diabetes technology in pregnancy. Obstet Gynecol 2024; 144 (05) 599-607
• 70 Bassi M, Franzone D, Dufour F. et al. Automated insulin delivery (AID) systems: use and efficacy in children and adults with type 1 diabetes and other forms of diabetes in Europe in early 2023. Life (Basel) 2023; 13 (03) 783
• 71 Stewart ZA, Yamamoto JM, Wilinska ME. et al. Adaptability of closed loop during labor, delivery, and postpartum: a secondary analysis of data from two randomized crossover trials in type 1 diabetes pregnancy. Diabetes Technol Ther 2018; 20 (07) 501-505
• 72 Kovatchev BP, Frasquet AC, Pryor EC. et al. Neural-net artificial pancreas: a randomized crossover trial of a first-in-class automated insulin delivery algorithm. Diabetes Technol Ther 2024; 26: 375-382
• 73 Sandborg J, Henriksson P, Larsen E. et al. Participants' engagement and satisfaction with a smartphone app intended to support healthy weight gain, diet, and physical activity during pregnancy: qualitative study within the HealthyMoms Trial. JMIR Mhealth Uhealth 2021; 9 (03) e26159
• 74 Vesco KK, Karanja N, King JC. et al. Healthy Moms, a randomized trial to promote and evaluate weight maintenance among obese pregnant women: study design and rationale. Contemp Clin Trials 2012; 33 (04) 777-785
• 75 Venkatesh KK, Joseph JJ, Swoboda C. et al. Multicomponent provider-patient intervention to improve glycaemic control in Medicaid-insured pregnant individuals with type 2 diabetes: clinical trial protocol for the ACHIEVE study. BMJ Open 2023; 13 (05) e074657
• 76 Gascoigne EL, Webster CM, Honart AW, Wang P, Smith-Ryan A, Manuck TA. Physical activity and pregnancy outcomes: an expert review. Am J Obstet Gynecol MFM 2023; 5 (01) 100758
• 77 Ehrlich SF, Maples JM, Barroso CS. et al. Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity. BMC Pregnancy Childbirth 2021; 21 (01) 420
• 78 Larsen B, Micucci S, Hartman SJ, Ramos G. Feasibility and acceptability of a counseling- and mHealth-based physical activity intervention for pregnant women with diabetes: The Fit for Two Pilot Study. JMIR Mhealth Uhealth 2020; 8 (10) e18915
• 79 Wang W, Cheng J, Song W, Shen Y. The effectiveness of wearable devices as physical activity interventions for preventing and treating obesity in children and adolescents: systematic review and meta-analysis. JMIR Mhealth Uhealth 2022; 10 (04) e32435
• 80 Leblalta B, Kebaili H, Sim R, Lee SWH. Digital health interventions for gestational diabetes mellitus: a systematic review and meta-analysis of randomised controlled trials. PLOS Digit Health 2022; 1 (02) e0000015
• 81 March of Dimes. Nowhere to Go: Maternity Care Deserts Across the U.S. (2022 Report). . Accessed April 2, 2024 at: https://www.marchofdimes.org/sites/default/files/2022-10/2022_Maternity_Care_Report.pdf
• 82 Sawyer MR, Jaffe EF, Naqvi M, Sarma A, Barth Jr WH, Goldfarb IT. Establishing better evidence on remote monitoring for postpartum hypertension: a silver lining of the coronavirus pandemic. AJP Rep 2020; 10 (03) e315-e318
• 83 Mhajna M, Schwartz N, Levit-Rosen L. et al. Wireless, remote solution for home fetal and maternal heart rate monitoring. Am J Obstet Gynecol MFM 2020; 2 (02) 100101
• 84 Hamm RF, Shkolnik K, Keren N. et al. Utilization of a wireless monitoring device to perform nonstress tests in high-risk pregnancies from home. Am J Obstet Gynecol 2023; 229 (04) 463-464
• 85 Schwartz N, Mhajna M, Moody HL. et al. Novel uterine contraction monitoring to enable remote, self-administered nonstress testing. Am J Obstet Gynecol 2022; 226 (04) 554.e1-554.e12
• 86 Mennickent D, Rodríguez A, Farías-Jofré M, Araya J, Guzmán-Gutiérrez E. Machine learning-based models for gestational diabetes mellitus prediction before 24-28 weeks of pregnancy: a review. Artif Intell Med 2022; 132: 102378
• 87 Quinn TP, Jacobs S, Senadeera M, Le V, Coghlan S. The three ghosts of medical AI: can the black-box present deliver?. Artif Intell Med 2022; 124: 102158
• 88 Durán JM, Jongsma KR. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J Med Ethics 2021;medethics-2020-106820
• 89 Liu Y, Deng G, Xu Z. et al. Jailbreaking ChatGPT via prompt engineering: an empirical study. arXiv:2305:13860v2; 2024
• 90 Johnson KB, Wei WQ, Weeraratne D. et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 2021; 14 (01) 86-93
• 91 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366 (6464) 447-453
• 92 Chan B. Black-box assisted medical decisions: AI power vs. ethical physician care. Med Health Care Philos 2023; 26 (03) 285-292
• 93 Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med 2018; 15 (11) e1002689
• 94 Cohen IG, Mello MM. HIPAA and protecting health information in the 21st century. JAMA 2018; 320 (03) 231-232
• 95 Porter E, Murphy M, O'Connor C. Chat GPT in dermatology: progressive or problematic?. J Eur Acad Dermatol Venereol 2023; 37 (07) e943-e944
• 96 Kolbinger FR, Veldhuizen GP, Zhu J, Truhn D, Kather JN. Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis. Commun Med (Lond) 2024; 4 (01) 71
• 97 Ibrahim H, Liu X, Denniston AK. Reporting guidelines for artificial intelligence in healthcare research. Clin Exp Ophthalmol 2021; 49 (05) 470-476
• 98 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3: 17
• 99 Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 2016; 375 (13) 1216-1219
• 100 Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med 2019; 132 (07) 795-801
• 101 Wang H, Fu T, Du Y. et al. Scientific discovery in the age of artificial intelligence. Nature 2023; 620 (7972) 47-60