Artificial intelligence is no longer a distant concept reserved for technology companies and research laboratories. It has arrived in the clinic, the delivery room, and the community health center. For midwives, this shift brings both exciting opportunity and legitimate questions — about how these tools work, which ones are trustworthy, and whether technology can ever truly complement the deeply human nature of midwifery care.
The short answer is yes — when implemented thoughtfully and ethically, AI and digital health tools have the potential to make midwifery practice safer, more efficient, and more accessible for the families who need it most. This post explores the key technologies shaping midwifery today, the challenges they bring, and how midwives can engage with innovation on their own terms.
What Does AI in Midwifery Actually Mean?
The term “artificial intelligence” covers a broad spectrum of technologies. In the context of midwifery and maternal health, AI most commonly refers to machine learning algorithms that analyze large datasets to identify patterns, predict outcomes, or support clinical decisions. These tools do not replace clinical judgment — they are designed to inform it.
At its core, AI in midwifery falls into several practical categories: predictive risk models that flag high-risk pregnancies earlier, remote monitoring systems that track maternal and fetal health between appointments, diagnostic support tools that assist with interpreting test results, and administrative systems that reduce documentation burden so midwives can spend more time on direct care.
Predictive Risk Modeling
One of the most impactful applications of AI in maternal health is the ability to predict complications before they become emergencies. Machine learning models trained on thousands of patient records can identify combinations of risk factors — such as blood pressure trends, age, prior pregnancy history, and socioeconomic indicators — that may precede conditions like preeclampsia, preterm labor, or postpartum hemorrhage.
For midwives working in under-resourced settings or managing large caseloads, these tools can serve as a valuable early warning system. Rather than relying solely on the periodic snapshot of a prenatal visit, predictive models draw from continuous data to flag patients who may need closer monitoring — potentially before symptoms even appear.
AI-Assisted Fetal Heart Rate Interpretation
Interpreting cardiotocography (CTG) traces — the continuous recordings of fetal heart rate and uterine contractions used during labor — has long been a skill that requires significant training and experience. Studies have shown that inter-observer variability in CTG interpretation is high, meaning two experienced clinicians may read the same trace differently.
AI tools trained on large CTG datasets can now provide a second layer of analysis, helping identify subtle patterns associated with fetal distress. These are not autonomous decision-makers but support tools that flag traces for closer human review. The goal is to reduce the chance that a concerning pattern is missed during a busy labor ward shift.
Digital Tools Midwives Are Using Right Now
Beyond AI specifically, a broader suite of digital technologies is already changing how midwives deliver care day to day. Understanding these tools helps midwives make informed decisions about which to adopt and how to integrate them without compromising the relationship-centered model at the heart of their practice.
Remote Patient Monitoring Devices
Wearable and home-based monitoring devices allow pregnant individuals to track blood pressure, fetal heart rate, blood glucose, and other vital measurements from home. The data is transmitted directly to their midwife, enabling continuous oversight between appointments. This is especially valuable for managing conditions like gestational hypertension or gestational diabetes, where trends over time matter more than any single reading.
For midwives already familiar with telehealth and virtual prenatal care models, remote monitoring is a natural complement — adding a physiological data layer to what would otherwise be a conversation-only virtual visit.
Electronic Health Records and Smart Documentation
Modern electronic health record (EHR) systems do far more than store notes. AI-powered EHR features can automatically flag abnormal lab values, suggest evidence-based care pathways, prompt documentation of key screening questions, and generate referral summaries. For midwives in busy clinical environments, these features reduce cognitive load and help ensure that nothing falls through the cracks.
Clinical Decision Support Systems
Clinical decision support (CDS) tools integrate with EHR platforms to deliver real-time guidance at the point of care. When a midwife enters a patient’s blood pressure reading, for example, a CDS tool can immediately cross-reference it against clinical thresholds and guidelines, prompting next steps if the reading suggests a concern. These systems are designed to support — not override — the midwife’s expertise and the patient’s informed preferences.
Digital Education and Training Tools
AI is also transforming how midwives learn and develop professionally. Simulation platforms powered by machine learning can adapt to a learner’s skill level, presenting increasingly complex scenarios as competency grows. Online learning systems track performance patterns and suggest areas for focused review. For midwifery students and those pursuing continuing education, these tools offer more personalized and efficient pathways to competency. Exploring the full range of these opportunities is part of understanding the evolving career pathways available to midwives today.
Ethical Considerations Midwives Must Know
The promise of AI in midwifery is real — but so are the risks. Midwives have both a professional and ethical responsibility to engage critically with these technologies rather than adopting them uncritically.
Algorithmic Bias and Health Equity
AI models are only as good as the data they are trained on. If the training datasets underrepresent certain populations — Black women, Indigenous communities, low-income families, or people in rural settings — the resulting models may perform less accurately for those very groups. Given that these communities already face the greatest maternal health disparities, deploying a biased AI tool in their care could compound existing inequities rather than reduce them.
This is not a hypothetical risk. Research has documented cases where health algorithms trained predominantly on data from white, higher-income populations performed significantly worse for minority patients. Midwives should ask vendors directly: who was in the training data? Has this tool been validated across diverse populations? What does the evidence say about its performance for communities like the ones I serve?
This concern connects directly to the broader conversation about addressing racial disparities in maternal health — a challenge that AI, if poorly implemented, risks making worse.
Data Privacy and Informed Consent
AI tools collect, store, and analyze sensitive health data. Patients have the right to know how their data is being used, who has access to it, and whether it may be shared with third parties including technology vendors or researchers. Midwives integrating AI tools into their practice must ensure those tools are compliant with applicable data protection laws and that patients provide meaningful informed consent — not just a signature on a form.
The Risk of Over-Reliance
There is a legitimate concern that heavy reliance on algorithmic tools could erode clinical skills over time, particularly among newer practitioners. If a midwife is accustomed to having a CDS tool flag concerns, what happens when that tool is unavailable or malfunctions? Maintaining sharp, independent clinical judgment must remain the foundation — AI is a tool in that process, not a substitute for it.
Keeping the Human Heart of Midwifery Intact
Perhaps the most important question midwives face in the age of AI is not “should we use this technology?” but “how do we use it without losing what makes midwifery irreplaceable?”
The midwifery model is built on relationship, presence, and trust. A machine can analyze a CTG trace or flag a rising blood pressure trend — but it cannot hold a laboring woman’s hand, read the fear in her eyes, or advocate for her wishes when the system pushes back. It cannot build the kind of therapeutic relationship where a patient feels safe enough to share that she has been struggling with her mental health, or that her home situation is unsafe.
Technology should free midwives to do more of what only they can do — by reducing administrative burden, flagging data patterns that might otherwise go unnoticed, and extending their reach to families in remote or underserved communities. As explored in how midwives are shaping the future of maternal care, the midwifery profession has always adapted to new contexts while preserving its core values. AI is simply the latest context.
Centering the Patient in Technology Decisions
Every decision about integrating a new technology should begin with a patient-centered question: does this tool improve the experience and outcomes for the families I serve? Does it enhance my ability to provide individualized, respectful care — or does it risk reducing that care to data points and alerts? Midwives are uniquely well-positioned to advocate for technologies that truly serve their patients, and to push back on those that do not.
What the Future Holds for AI and Midwifery
The pace of development in digital health is rapid, and the tools available to midwives will continue to evolve significantly over the coming years. Several emerging areas are particularly worth watching.
AI in Low-Resource Global Settings
Some of the most exciting AI applications in midwifery are being developed specifically for low-resource settings — the very environments where the global midwife shortage is most acute. Point-of-care ultrasound devices paired with AI interpretation tools, for example, can help midwives in remote areas conduct fetal assessments without requiring specialist radiologists. Voice-based clinical decision support accessible via basic mobile phones is being tested in sub-Saharan Africa and South Asia.
For an organization like Midwives for Midwives, whose mission centers on strengthening midwifery capacity in underserved communities, these developments are particularly significant. AI tools designed for global equity — not just wealthy health systems — represent a real opportunity to extend skilled care to the families who need it most. You can learn more about our mission and the communities we serve on our Vision, Mission, and Purpose page.
Multimodal AI and Continuous Monitoring
The next generation of AI tools will not analyze a single data stream in isolation. Multimodal AI systems will integrate data from wearables, EHRs, patient-reported outcomes, and even social determinants of health to generate a comprehensive, continuously updated picture of a patient’s risk profile. For midwives, this means less time hunting through fragmented records and more time acting on synthesized, actionable insight.
AI-Supported Midwifery Education Globally
Scaling midwifery education — particularly in regions facing severe workforce shortages — is one of the most pressing challenges in global maternal health. AI-powered simulation and competency assessment tools offer a pathway to high-quality training at scale, without requiring every student to be co-located with expert faculty. This does not replace mentorship and supervised clinical experience, but it can meaningfully expand the pipeline of skilled midwives entering the workforce.
How Midwives Can Engage with AI on Their Own Terms
Engaging with AI in midwifery does not require a technology background. It requires critical thinking, a clear set of professional values, and a willingness to ask the right questions. Here are practical starting points for midwives at any stage of their career.
Stay Informed Through Trusted Sources
The International Confederation of Midwives (ICM) and the World Health Organization’s Digital Health division both publish guidance on technology in maternal and reproductive health. Staying current with these resources helps midwives evaluate new tools against established professional standards.
Participate in Implementation Conversations
When healthcare organizations or governments adopt new AI tools for maternal care, midwives must be at the table — not as passive recipients of technology decisions made by administrators or engineers, but as active advocates for their patients and their profession. The midwifery advocacy model applies here just as it does in policy and regulatory contexts.
Share Knowledge Within the Community
The midwifery community’s greatest strength has always been its commitment to peer learning and collective support. Midwives who have experience with specific AI tools — good or bad — have a responsibility to share those experiences with colleagues. Stronger communities and networks within the profession are the best safeguard against both uncritical adoption and reflexive rejection of technologies that could genuinely help.
Technology in Service of Midwifery — Not the Other Way Around
AI and digital health innovation are not threats to the midwifery model — they are tools that, in the right hands and with the right safeguards, can amplify everything midwifery stands for. Safer outcomes. Wider access. Earlier interventions. More time for the human work that no algorithm will ever replace.
The midwifery profession has navigated profound changes before and emerged stronger for it. The task now is to approach the technological transformation of healthcare with the same critical, patient-centered wisdom that has always defined this work — embracing what serves families, questioning what does not, and never losing sight of the irreplaceable human presence at the heart of every birth.
