Howard, L. M. & Khalifeh, H. Perinatal mental health: A review of progress and challenges. World Psychiatry 19, 313–327 (2020).
Google Scholar
Fawcett, E. J., Fairbrother, N., Cox, M. L., White, I. R. & Fawcett, J. M. The prevalence of anxiety disorders during pregnancy and the postpartum period: A multivariate Bayesian meta-analysis. J. Clin. Psychiatry. 80, (2019).
Luca, D. L. et al. Financial toll of untreated perinatal mood and anxiety disorders among 2017 births in the united states. Am. J. Public Health 110, 888–896 (2020).
Google Scholar
Fisher, S. D. Paternal mental health: Why is it relevant?. Am. J. Lifestyle Med 11, 200–211 (2016).
Google Scholar
Bruzelius, E. & Martins, S. S. US trends in drug overdose mortality among pregnant and postpartum persons, 2017-2020. JAMA 328, 2159–2161 (2022).
Google Scholar
Goldman-Mellor, S. & Margerison, C. E. Maternal drug-related death and suicide are leading causes of postpartum death in California. Obstet. Gynecol. 221, 489.e1–489.e9 (2019).
Kountanis, J. A. et al. Maternal deaths due to suicide and overdose in the state of michigan from 2008 to 2018. Am. J. Obstet. Gynecol. MFM 5, 100811 (2023).
Google Scholar
Cox, E. Q., Sowa, N. A., Meltzer-Brody, S. E. & Gaynes, B. N. The perinatal depression treatment cascade: Baby steps toward improving outcomes. J. Clin. Psychiatry 77, 1189–1200 (2016).
Google Scholar
Miller, M. L., Dupree, J., Monette, M. A., Lau, E. K. & Peipert, A. Health equity and perinatal mental health. Curr. Psychiatry Rep. 26, 460–469 (2024).
Google Scholar
Stade, E. C. et al. Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation. Npj Ment. Health Res 3, 12 (2024).
Google Scholar
Kay, E. S., Batey, D. S. & Mugavero, M. J. The HIV treatment cascade and care continuum: Updates, goals, and recommendations for the future. AIDS Res Ther. 13, 35 (2016).
Google Scholar
Centers for Disease Control. Births and natality. Updated 2024. Accessed Jan 25, (2025).
Wisner, K. L. et al. Onset timing, thoughts of self-harm, and diagnoses in postpartum women with screen-positive depression findings. JAMA Psychiatry 70, 490–498 (2013).
Google Scholar
Haight, S. C. et al. Racial and ethnic inequities in postpartum depressive symptoms, diagnosis, and care in 7 US jurisdictions. Health Aff. ((Millwood)) 43, 486–495 (2024).
Google Scholar
American College of Obstetrics and Gynecology Screening and diagnosis of mental health conditions during pregnancy and postpartum: ACOG clinical practice guideline no. 4. Obstet. Gynecol. 141, 1232–1261 (2023).
American College of Obstetrics and Gynecology. Implementing perinatal mental health screening. Web site. /programs/perinatal-mental-health/implementing-perinatal-mental-health-screening. Updated 2022. Accessed January 25, 2025.
Fairbrother, N., Corbyn, B., Thordarson, D. S., Ma, A. & Surm, D. Screening for perinatal anxiety disorders: Room to grow. J. Affect Disord. 250, 363–370 (2019).
Google Scholar
Fairbrother, N., Albert, A., Keeney, C., Tchir, D. & Cameron, R. B. Screening for perinatal OCD: A comparison of the DOCS and the EPDS. Assessment 30, 1028–1039 (2023).
Google Scholar
Philpott, L. F. Paternal perinatal mental health: Progress, challenges and future direction. Trends Urol. Men. Health 14, 14–18 (2023).
O’Brien, A. P. et al. New fathers’ perinatal depression and anxiety—Treatment options: An integrative review. Am. J. Men.’s. Health 11, 863–876 (2017).
Fisher, S. D., Walsh, T. & Wongwai, C. The importance of perinatal non-birthing parents’ mental health and involvement for family health. Semin Perinatol. 48, 151950 (2024).
Google Scholar
Walsh, T. B. & Garfield, C. F. Perinatal mental health: Father inclusion at the local, state, and national levels. Health Aff. ((Millwood)) 43, 590–596 (2024).
Google Scholar
Kwok, W. H., Zhang, Y. & Wang, G. Artificial intelligence in perinatal mental health research: A scoping review. Comput. Biol. Med. 177, 108685 (2024).
Google Scholar
Zhang, Y., Wang, S., Hermann, A., Joly, R. & Pathak, J. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J. Affect Disord. 279, 1–8 (2021).
Google Scholar
Amit, G. et al. Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy Childbirth 21, 630–638 (2021).
Google Scholar
Obradovich, N. et al. Opportunities and risks of large language models in psychiatry. NPP Digit Psychiatry Neurosci. 2, 8 (2024).
Google Scholar
Ayre, K. et al. Developing a natural language processing tool to identify perinatal self-harm in electronic healthcare records. PLoS one 16, e0253809 (2021).
Google Scholar
Shatte, A. B. R., Hutchinson, D. M., Fuller-Tyszkiewicz, M. & Teague, S. J. Social media markers to identify fathers at risk of postpartum depression: A machine learning approach. Cyberpsychol Behav. Soc. Netw. 23, 611–618 (2020).
Google Scholar
Dhankar, A. & Katz, A. Tracking pregnant women’s mental health through social media: An analysis of reddit posts. JAMIA open 6, ooad094 (2023).
Google Scholar
Tierney, A. A. et al. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catalyst. 5, CAT.23.0404. (2024).
Basílio, G. et al. AI-driven early mental health screening with limited data: Analyzing selfies of pregnant women. (2024).
Hsieh, W. et al. Patients’ perceptions of perinatal depression screening: A qualitative study: Study examines perinatal depression screening. Health Aff. 40, 1612–1617 (2021).
Hutner, L. A. et al. Cultivating mental health education in obstetrics and gynecology: A call to action. Am. J. Obstet. Gynecol. MFM 3, 100459 (2021).
Google Scholar
Koire, A., Suleiman, M., Teslyar, P. & Liu, C. H. Prevalence of community perinatal psychiatrists in the US. JAMA Netw. Open 7, e2426465 (2024).
Google Scholar
Analysis NCfHW. Behavioral health workforce, 2023. (2023).
Branquinho, M. et al. Effectiveness of psychological interventions in the treatment of perinatal depression: A systematic review of systematic reviews and meta-analyses. J. Affect Disord. 291, 294–306 (2021).
Google Scholar
Meltzer-Brody, S. & Jones, I. Optimizing the treatment of mood disorders in the perinatal period. Dialogues Clin. Neurosci. 17, 207–218 (2015).
Google Scholar
Avalos, L. A. et al. Racial-ethnic differences in treatment initiation for new diagnoses of perinatal depression. Psychiatr. Serv. 74, 341–348 (2023).
Google Scholar
Dembosky, A. No title. A Humane Approach To Caring For New Mothers In Psychiatric Crisis: Article examines inpatient psychiatric care for new mothers in England’s mother and baby care units. (2021).
Hutner, L. A., Catapano, L. A., Nagle-Yang, S. M., Williams, K. E. & Osborne, L. M. Textbook of women’s reproductive mental health. American Psychiatric Pub. (2021).
Wiley, R. Updates to interventions in treatment of perinatal depression and anxiety. Curr. Obstet. Gynecol. Rep. 13, 1–9 (2024).
Taouk, L. H., Matteson, K. A., Stark, L. M. & Schulkin, J. Prenatal depression screening and antidepressant prescription: Obstetrician-gynecologists’ practices, opinions, and interpretation of evidence. Arch. Women’s. Ment. Health 21, 85–91 (2018).
Kittel-Schneider, S., Quednow, B. B., Leutritz, A. L., McNeill, R. V. & Reif, A. Parental ADHD in pregnancy and the postpartum period–A systematic review. Neurosci. Biobehav. Rev. 124, 63–77 (2021).
Google Scholar
Lewis, A. & Khong, T. K. ADHD medication shortages: More than just a supply issue. Drug Ther. Bull. 62, 18 (2024).
Google Scholar
McLeish, J., Ayers, S. & McCourt, C. Community-based perinatal mental health peer support: A realist review. BMC Pregnancy Childbirth 23, 570 (2023).
Google Scholar
The Policy Center for Maternal Mental Health. Maternal mental health certified peer support. Updated 2024. Accessed January 10, 2025.
Quiray, J. et al. The role of doulas in supporting perinatal mental health–a qualitative study. Front. Psychiatry 15, 1272513 (2024).
Google Scholar
Thomas, M., Ammann, G., Brazier, E., Noyes, P. & Maybank, A. Doula services within a healthy start program: Increasing access for an underserved population. Matern Child Health J. 21, 59–64 (2017).
Google Scholar
Yang, S., Lee, J., Sezgin, E., Bridge, J. & Lin, S. Clinical advice by voice assistants on postpartum depression: Cross-sectional investigation using apple siri, amazon alexa, google assistant, and microsoft cortana. JMIR Mhealth Uhealth 9, e24045 (2021).
Google Scholar
Sezgin, E., Chekeni, F., Lee, J. & Keim, S. Clinical accuracy of large language models and google search responses to postpartum depression questions: Cross-sectional study. J. Med Internet Res 25, e49240 (2023).
Google Scholar
Deutscher, M. Report: Amazon will use anthropic’s claude LLM series to power upcoming alexa upgrade. Updated 2024. Accessed January 10, 2025.
Deichen Hansen, M. E. et al. The role of perinatal psychiatry access programs in advancing mental health equity. Gen. Hosp. Psychiatry 82, 75–85 (2023).
Google Scholar
Mancinelli, E., Magnolini, S., Gabrielli, S. & Salcuni, S. A chatbot (juno) prototype to deploy a behavioral activation intervention to pregnant women: Qualitative evaluation using a multiple case study. JMIR Form. Res 8, e58653 (2024).
Google Scholar
Suharwardy, S. et al. Feasibility and impact of a mental health chatbot on postpartum mental health: A randomized controlled trial. AJOG Glob. Rep. 3, 100165 (2023).
Google Scholar
Soula. Soula: AI-powered women’s wellness companion. Updated 2024. Accessed January 10, 2025.
Waymouth, M., James, K. & Uscher-Pines, L. Advancing equity in maternal health with virtual doula care. JAMA Health Forum 5, e234833 (2024).
Google Scholar
Logue, T. C. et al. Continuation of psychiatric medications during pregnancy. J. Matern Fetal Neonatal Med 36, 2171288 (2023).
Google Scholar
Nillni, Y. I., Mehralizade, A., Mayer, L. & Milanovic, S. Treatment of depression, anxiety, and trauma-related disorders during the perinatal period: A systematic review. Clin. Psychol. Rev. 66, 136–148 (2018).
Google Scholar
Cuijpers, P. & Karyotaki, E. The effects of psychological treatment of perinatal depression: An overview. Arch. Women’s. Ment. Health 24, 801–806 (2021).
Spytska, L. The use of artificial intelligence in psychotherapy: Development of intelligent therapeutic systems. BMC Psychol. 13, 175 (2025).
Google Scholar
Hurwitz, E. et al. Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using the all of us research program data set: Cross-sectional study. JMIR mHealth uHealth 12, e54622 (2024).
Google Scholar
Gidén, K. et al. Remission or persistence? A prediction tool to identify women at risk for Long-Term depressive symptoms postpartum. Depress Anxiety 2024, 7734542 (2024).
Google Scholar
Wong, E. F. et al. Evaluating bias-mitigated predictive models of perinatal mood and anxiety disorders. JAMA Netw. Open 7, e2438152 (2024).
Google Scholar
Dlugatch, R., Georgieva, A. & Kerasidou, A. Trustworthy artificial intelligence and ethical design: Public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care. BMC Med Ethics 24, 42 (2023).
Google Scholar
Turchioe, M. R., Hermann, A. & Benda, N. C. Recentering responsible and explainable artificial intelligence research on patients: Implications in perinatal psychiatry. Front Psychiatry 14, 1321265 (2023).
Google Scholar
Richardson, B. & Gilbert, J. A framework for fairness: A systematic review of existing fair AI solutions. (2021).
Representative Sharice Davis, Senator Tina Smith, Black Maternal Health Caucus. S.1599 – the momnibus: Data to save moms act. 2023;Senate – Health, Education, Labor, and Pensions (118).
Celi, L. A. et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review. PLOS Digit Health 1, e0000022 (2022).
Google Scholar
Anisuzzaman, D. M., Malins, J. G., Friedman, P. A. & Attia, Z. I. Fine-Tuning Large Language Models for Specialized Use Cases. Mayo Clin. Proc. Digit Health 3, 100184 (2025).
Google Scholar
Sahiner, B., Chen, W., Samala, R. K. & Petrick, N. Data drift in medical machine learning: Implications and potential remedies. Br. J. Radio. 96, 20220878 (2023).
Abdul Razak, M. S., Nirmala, C. R., Sreenivasa, B. R., Lahza, H. & Lahza, H. F. M. A survey on detecting healthcare concept drift in AI/ML models from a finance perspective. Front. Artif. Intell. 5, 955314 (2023).
Uren, V. & Edwards, J. S. Technology readiness and the organizational journey towards AI adoption: An empirical study. Int J. Inf. Manag. 68, 102588 (2023).
Gomez Rossi, J., Feldberg, B., Krois, J. & Schwendicke, F. Evaluation of the clinical, technical, and financial aspects of cost-effectiveness analysis of artificial intelligence in medicine: Scoping review and framework of analysis. JMIR Med Inf. 10, e33703 (2022).
Su, C. NVIDIA unveils its most affordable generative AI supercomputer. 2024. Accessed Jan 25, (2025).
Sun, L., Jiang, X., Ren, H. & Guo, Y. Edge-cloud computing and artificial intelligence in internet of medical things: Architecture, technology and application. IEEE Access. (2020);PP:1. https://doi.org/10.1109/ACCESS.2020.2997831.
Agency E. P. EPA research: Environmental justice and air pollution. 2024;2025. https://www.epa.gov/ej-research/epa-research-environmental-justice-and-air-pollution.
Mills, R. J. Patient survey shows unresolved tension over health data privacy. 2022;2025. https://www.ama-assn.org/press-center/press-releases/patient-survey-shows-unresolved-tension-over-health-data-privacy.
Habbal, A., Ali, M. K. & Abuzaraida, M. A. Artificial intelligence trust, risk and security management (AI TRiSM): Frameworks, applications, challenges and future research directions. Expert Syst. Appl 240, 122442 (2024)
Ugwu, A., Gao, X., Ugwu, J. & Chang, V. Ethical implications of AI in healthcare data: A case study using healthcare data breaches from the US department of health and human services breach portal between 2009-2021. 2022:343–349.
Rights (OCR) OfC. Enforcement data. Updated 2008. Accessed Jan 25, 2025.
Ruiz, R. American Psychological Association sounds alarm over certain AI chatbots. 2025; (2025).
Meinke, A. et al. Frontier models are capable of in-context scheming. arXiv preprint arXiv:2412.04984. (2024).
Health CfDaR. Artificial intelligence and machine learning (AI/ML)-enabled medical devices. FDA. 2024. Accessed Jan 25, 2025.
Aboy, M., Crespo, C. & Stern, A. Beyond the 510(k): The regulation of novel moderate-risk medical devices, intellectual property considerations, and innovation incentives in the FDA’s de novo pathway. npj Digital Med. 7, 29 (2024).
The Policy Center for Maternal Mental Health. The first digital therapeutic for maternal mental health is approved by the FDA. The Policy Center for Maternal Mental Health Web site. Updated 2024. Accessed January 10, 2025.
van Kessel, R. et al. Mapping factors that affect the uptake of digital therapeutics within health systems: Scoping review. J. Med Internet Res 25, e48000 (2023).
Google Scholar
The White House. Removing barriers to American leadership in artificial intelligence. Updated 2025. Accessed Jan 25, 2025.
Future of Life Institute. EU artificial intelligence act | up-to-date developments and analyses of the EU AI act. Accessed Jan 25, 2025.
link