By Chukwuma I. Onyeije, MD
Introduction: The Preeclampsia Challenge and the Promise of AI
Every 7 minutes, a woman dies from complications of pregnancy and childbirth-related hypertensive disorders. Preeclampsia alone affects 5-8% of all pregnancies globally, contributing to over 70,000 maternal deaths and 500,000 fetal and neonatal deaths annually. Behind these statistics are real women, real families, and real tragedies—many of which could be prevented with early detection and appropriate management.
As physicians who understand both the clinical complexities of preeclampsia and the transformative potential of technology, we stand at a unique intersection. We witness the limitations of current screening methods and the devastating consequences when this condition progresses undetected or untreated. Simultaneously, we recognize the unprecedented capabilities of artificial intelligence (AI) and large language models (LLMs) to analyze complex data, identify subtle patterns, and support clinical decision-making in ways previously unimaginable.
This convergence of medicine and technology creates an opportunity to fundamentally transform how we predict, diagnose, and manage preeclampsia. By leveraging AI and LLMs, we can move beyond conventional risk stratification to develop more personalized, precise, and proactive approaches—ultimately saving lives and improving outcomes for countless mothers and their babies.
Understanding Preeclampsia: The Biological Basis for AI Applications
To effectively apply AI solutions to preeclampsia, we must first understand the complex pathophysiology that underpins this condition. Preeclampsia begins with abnormal placental development in early pregnancy. Incomplete trophoblast invasion of maternal spiral arteries creates a hypoxic environment that triggers a cascade of molecular events.
This placental dysfunction leads to a critical imbalance of angiogenic factors—specifically, decreased placental growth factor (PlGF) and increased soluble fms-like tyrosine kinase-1 (sFlt-1). The resulting sFlt-1/PlGF ratio has emerged as one of the most reliable biomarkers for preeclampsia prediction and diagnosis.
The angiogenic imbalance ultimately causes widespread maternal endothelial dysfunction—the hallmark of preeclampsia—manifesting clinically as hypertension, proteinuria, and in severe cases, damage to multiple organ systems including the liver, kidneys, brain, and hematological system.
What makes preeclampsia particularly challenging from a clinical perspective is its variable presentation. Some women develop textbook symptoms, while others present with atypical features or remain asymptomatic until the condition becomes severe. This variability creates an ideal scenario for AI applications, which excel at identifying subtle patterns and correlations across diverse presentations and datasets.
By understanding these underlying biological mechanisms, we can identify specific targets for data collection and AI model development, including:
- Longitudinal tracking of angiogenic biomarkers (sFlt-1, PlGF)
- Patterns of maternal blood pressure changes
- Subtle alterations in organ function tests
- Variations in ultrasound parameters
- Changes in maternal symptoms over time
The Current Landscape of Preeclampsia Screening and its Limitations
Current approaches to preeclampsia screening have evolved significantly over recent decades, yet still fall short of the precision and personalization needed to effectively prevent adverse outcomes. Traditional risk assessment relies primarily on maternal factors such as age, BMI, medical history, and obstetric history. While helpful for initial stratification, this approach identifies only 30-40% of women who will develop preeclampsia.
The introduction of first-trimester combined screening marked a significant advancement. By integrating maternal factors with blood biomarkers (PlGF, PAPP-A) and ultrasound parameters (mean arterial pressure, uterine artery pulsatility index), detection rates improved substantially. The SPREE trial demonstrated that this approach detected approximately 75% of cases of preterm preeclampsia, compared to just 40% using NICE guidelines based on maternal factors alone.
Further refinements occurred with the addition of the sFlt-1/PlGF ratio, particularly for prediction in later trimesters and for short-term prognosis. A ratio over 38 has been associated with a significantly increased risk of developing preeclampsia within four weeks.
Despite these improvements, several limitations persist:
- Imperfect Accuracy: Even the most advanced screening methods miss a significant percentage of women who will develop preeclampsia.
- Limited Individualization: Current models provide risk categories rather than truly personalized risk assessments.
- Static Assessment: Most screening is performed at specific timepoints, missing the opportunity for continuous monitoring and reassessment.
- Implementation Barriers: Advanced screening methods often require specialized equipment, laboratory testing, and expertise that may not be available in all settings.
- Disparities in Performance: Some screening tools perform differently across racial and ethnic groups, potentially exacerbating existing disparities in maternal outcomes.
These limitations create a clear mandate for innovation, particularly through AI and LLM applications that can overcome these challenges and push the boundaries of what’s possible in preeclampsia risk prediction and management.
AI and LLMs: Powerful Tools for Enhancing Preeclampsia Management
Improved Risk Prediction with AI/ML
Artificial intelligence and machine learning offer capabilities that extend far beyond traditional statistical methods in preeclampsia risk assessment. While conventional approaches like logistic regression can identify linear relationships between a limited number of variables, AI algorithms can analyze complex, high-dimensional datasets to detect subtle patterns and non-linear interactions that would otherwise remain hidden.
This capability is particularly valuable for preeclampsia, where risk likely emerges from the complex interplay of genetic factors, maternal characteristics, environmental influences, and placental development. Advanced neural networks and ensemble methods can integrate diverse data types—from structured clinical measurements to unstructured notes, genomic data, and even environmental factors—to generate more comprehensive risk profiles.
Early research in this area has shown promising results. Several machine learning models have demonstrated superior performance compared to conventional methods, with improvements in sensitivity, specificity, and area under the curve (AUC). More importantly, these models can provide continuously updated, personalized risk scores as new data becomes available throughout pregnancy.
The clinical implications are significant. More accurate risk stratification enables more targeted interventions, particularly low-dose aspirin prophylaxis, which the ASPRE trial showed could reduce the risk of preterm preeclampsia by approximately 60% when initiated before 16 weeks of gestation. The ability to identify high-risk women earlier and with greater precision could substantially increase the impact of this preventative measure.
LLMs for Enhanced Clinical Decision Support
Large language models represent a revolutionary advance in artificial intelligence with unique applications for preeclampsia management. These sophisticated models can process, synthesize, and generate human-like text based on vast amounts of training data, offering several capabilities that could transform clinical practice:
Accessing and Synthesizing Information: LLMs can analyze millions of medical articles, clinical guidelines, and case reports to provide clinicians with instant access to relevant information. For a condition like preeclampsia, where management guidelines evolve frequently and medical literature expands rapidly, this capability ensures that clinical decisions are informed by the most current evidence.
Generating Personalized Care Recommendations: By analyzing a patient’s specific risk factors, clinical presentation, and ongoing monitoring data, LLMs could suggest tailored management plans, including appropriate monitoring intervals, laboratory testing, medication adjustments, and timing of delivery. This level of personalization moves beyond the one-size-fits-all approach of standard guidelines.
Identifying Atypical Presentations: Preeclampsia can present in unusual ways that may not immediately trigger clinical concern. LLMs trained on large datasets of clinical notes and case reports could flag subtle or atypical symptoms that warrant further investigation, reducing missed or delayed diagnoses.
Supporting Clinical Documentation and Communication: LLMs can assist in generating comprehensive clinical notes, discharge summaries, and interdisciplinary communication, ensuring that critical information about a patient’s preeclampsia status and management plan is accurately conveyed across the care team.
AI-Powered Continuous Monitoring
One of the most promising applications of AI in preeclampsia management is continuous monitoring and early warning system development. Traditional prenatal care involves intermittent assessment at scheduled visits, potentially missing critical changes between appointments. AI systems paired with wearable devices and remote monitoring tools could transform this paradigm.
Imagine a scenario where a pregnant woman wears a device that continuously monitors her blood pressure, heart rate, and other vital signs. An AI algorithm analyzes this data in real-time, identifying subtle patterns that may indicate developing preeclampsia days or even weeks before clinical symptoms would trigger concern during a routine visit.
These systems could detect meaningful changes in vital sign patterns, such as:
- Loss of the normal nocturnal dip in blood pressure
- Increased blood pressure variability
- Subtle changes in heart rate or heart rate variability
- Alterations in activity patterns or sleep quality
When concerning patterns emerge, the system could trigger appropriate clinical responses—ranging from increased monitoring to immediate evaluation—based on the severity of the detected abnormalities.
Research in this area has already shown promise. Several studies have demonstrated that machine learning algorithms can predict adverse pregnancy outcomes, including preeclampsia, based on longitudinal vital sign data with greater accuracy than conventional methods. As these technologies mature and become more widely accessible, they have the potential to fundamentally change how we monitor high-risk pregnancies.
LLMs for Improved Patient Education and Communication
Beyond clinical applications, LLMs offer powerful tools for enhancing patient education and engagement. Preeclampsia management requires active patient participation, including symptom monitoring, medication adherence, and timely reporting of concerns. LLMs can facilitate this partnership in several ways:
Generating Tailored Educational Materials: LLMs can create personalized information about preeclampsia that addresses each patient’s specific risk factors, literacy level, language preference, and cultural context. Rather than generic handouts, women could receive information directly relevant to their situation, increasing comprehension and retention.
Providing On-Demand Information Access: AI-powered chatbots or virtual assistants could answer patient questions about preeclampsia symptoms, monitoring protocols, or medication side effects at any time, reducing anxiety and improving adherence to care plans.
Facilitating Shared Decision Making: By presenting complex medical information in accessible ways, LLMs could enhance patients’ ability to participate in decisions about their care, particularly around critical issues like timing of delivery when preeclampsia is diagnosed.
Supporting Remote Monitoring Adherence: LLMs could generate personalized reminders and motivational messages to encourage consistent blood pressure monitoring, medication adherence, and symptom tracking—crucial components of successful preeclampsia management.
Navigating the Challenges and Ethical Considerations
While the potential benefits of AI and LLMs in preeclampsia management are substantial, their implementation comes with significant challenges and ethical considerations that must be thoughtfully addressed.
Data Privacy and Security
AI and LLM applications in preeclampsia management require access to sensitive patient data, including medical history, genetic information, and real-time monitoring data. Ensuring the privacy and security of this information is paramount. Robust encryption, secure data storage solutions, and transparent data governance policies must be fundamental components of any AI implementation in this space.
Furthermore, patients must provide informed consent for their data to be used in AI systems, which requires clear communication about how their information will be collected, stored, analyzed, and protected. This becomes particularly important in the context of continuous monitoring, where the volume and intimacy of collected data increase substantially.
Algorithmic Bias and Fairness
Preeclampsia already exhibits significant disparities in outcomes across racial and ethnic groups, with Black women experiencing substantially higher rates of preeclampsia-related complications and mortality. AI systems trained on biased data could inadvertently perpetuate or even amplify these disparities.
To address this concern, AI models must be developed using diverse, representative datasets and validated across different demographic groups. Regular equity audits should be conducted to identify and mitigate potential bias, and performance metrics should be disaggregated by race, ethnicity, and socioeconomic status to ensure the benefits of these technologies are equitably distributed.
Regulatory Hurdles and Clinical Validation
AI and LLM applications for preeclampsia management constitute medical devices that require appropriate regulatory oversight. Navigating the FDA approval process for these technologies can be complex and time-consuming, but it’s essential for ensuring safety and efficacy.
Before widespread implementation, these systems must undergo rigorous clinical validation through well-designed trials that demonstrate their impact on meaningful clinical outcomes, not just on intermediate measures or statistical performance. This validation should include assessment across diverse clinical settings, from academic medical centers to community hospitals and resource-limited environments.
Integration with Existing Clinical Workflows
Even the most powerful AI tools will have limited impact if they cannot be seamlessly integrated into existing clinical workflows. Effective implementation requires thoughtful consideration of:
- Integration with electronic health record systems
- User interface design for busy clinical environments
- Minimizing additional documentation burden
- Clear protocols for responding to AI-generated alerts
- Training requirements for clinical staff
Successful integration will likely require interdisciplinary collaboration among clinicians, data scientists, user experience designers, and health system administrators.
The Importance of Human Oversight
Perhaps most importantly, we must maintain a clear understanding that AI and LLMs should augment, not replace, clinical judgment. These technologies can process vast amounts of data and identify subtle patterns, but they lack the contextual understanding, ethical reasoning, and human connection that are central to medical practice.
Effective implementation models will position AI as a “second opinion” or decision support tool, with clinicians maintaining ultimate responsibility for interpretation and decision-making. This approach leverages the complementary strengths of AI (data processing, pattern recognition) and human clinicians (contextual understanding, ethical reasoning, empathetic communication).
The Future is Coded: Opportunities for “Doctors Who Code”
As physicians with coding skills, we occupy a unique position at the intersection of clinical medicine and technology. This dual expertise allows us to bridge the gap between these domains, ensuring that AI and LLM applications are clinically relevant, technically sound, and ethically implemented.
Several specific areas present particularly promising opportunities for contribution:
Developing and Validating Prediction Models
Clinician-developers can lead efforts to create more accurate, equitable, and clinically useful preeclampsia prediction models. This work might include:
- Curating high-quality, diverse training datasets
- Selecting appropriate features based on clinical expertise
- Designing models that balance complexity with interpretability
- Conducting thorough validation across diverse populations
- Creating protocols for model updating and maintenance
Building Clinical Decision Support Tools
Translating predictive models into useful clinical tools requires deep understanding of clinical workflows and decision-making processes. Potential projects include:
- Developing LLM-powered EHR integrations that provide contextual risk information
- Creating visualization tools that effectively communicate risk estimates
- Designing smart alert systems that minimize alarm fatigue
- Building documentation assistants that reduce administrative burden
Creating Patient-Facing Applications
The patient experience represents another critical area for innovation, with opportunities to:
- Develop accessible, engaging education platforms powered by LLMs
- Design remote monitoring systems with user-friendly interfaces
- Create multilingual conversational AI tools for symptom monitoring
- Build shared decision-making tools for complex care decisions
Conducting Implementation Research
Beyond development, clinician-coders can lead research on effective implementation strategies, including:
- Evaluating various approaches to clinical integration
- Assessing the impact on workflow and clinician experience
- Measuring effects on clinical outcomes and health disparities
- Developing best practices for AI implementation in obstetric care
Addressing Ethical and Regulatory Challenges
Finally, physicians with technical expertise can contribute significantly to the ethical and regulatory framework for these technologies by:
- Developing standards for model documentation and transparency
- Creating protocols for consent and data governance
- Advising regulatory bodies on appropriate oversight approaches
- Leading education efforts to promote responsible AI use
Conclusion: Empowering Safer Pregnancies Through Innovation
The integration of AI and LLMs into preeclampsia management represents more than a technological advance—it offers a fundamental reimagining of how we approach this critical health challenge. By moving from episodic risk assessment to continuous monitoring, from population-based guidelines to personalized care plans, and from reactive management to proactive prevention, these technologies could significantly reduce the burden of preeclampsia on mothers and babies worldwide.
As physicians who code, we have both the opportunity and the responsibility to lead this transformation. Our clinical expertise ensures that technology development remains grounded in patient needs and clinical realities. Our technical skills enable us to translate these needs into effective solutions. And our ethical commitment guides responsible implementation that advances health equity and patient autonomy.
The code we write today could save lives tomorrow—creating a future where no woman dies from a preventable complication of pregnancy, and where every mother has the opportunity to safely welcome her child into the world.
A Call to Action
From Chukwuma I. Onyeije, MD
The statistics on preeclampsia-related maternal mortality should shake us to our core. Behind each number is a mother who didn’t get to see her child grow up, a family forever changed by preventable tragedy. As both physicians and technologists, we have a unique opportunity—indeed, a moral imperative—to apply our dual expertise toward solving this challenge.
I invite each of you to consider how your specific skills and knowledge might contribute to this effort. Whether you’re an obstetrician who dabbles in Python, a radiologist building deep learning models, or a family physician creating patient education apps, your perspective is valuable and needed.
Join our community at Doctors Who Code Blog where you can connect with like-minded clinicians, share your projects, and find collaboration opportunities. For those specifically interested in preeclampsia innovation, I encourage you to visit Preeclampsia Watch where we’re building a knowledge base and resource hub dedicated to technology solutions for this critical maternal health challenge.
Start small if necessary—analyze your own clinic’s data, build a simple tool for your practice, or join an existing open-source project. Connect with others doing similar work, share your successes and failures, and contribute to the growing community at the intersection of medicine and technology.
The transformation of preeclampsia care won’t happen overnight, nor will it come from a single breakthrough. Rather, it will emerge from the collective efforts of many committed individuals applying their dual expertise to various aspects of this complex problem. Your contribution, however modest it may seem, could be the missing piece that makes all the difference.
Our patients deserve nothing less than our full commitment to this cause. Let’s code for safer pregnancies—one function, one algorithm, one life at a time.
Dr. Chukwuma I. Onyeije is a maternal-fetal medicine specialist with over 20 years of clinical experience and a passionate advocate for applying technology to improve maternal outcomes. He leads the AI for Maternal Health Initiative and serves as a technical advisor to several healthcare startups focused on pregnancy complications.
This article was published on March 30, 2025 and last updated on March 30, 2025.
References
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- Rana, S., Lemoine, E., Granger, J., & Karumanchi, S. A. (2023). Preeclampsia: Pathophysiology, Challenges, and Perspectives. Circulation Research, 126(12), 1800-1819.
- Rolnik, D. L., Wright, D., Poon, L. C., et al. (2021). ASPRE trial: performance of screening for preterm pre-eclampsia. Ultrasound in Obstetrics & Gynecology, 50(4), 492-495.
- Zeisler, H., Llurba, E., Chantraine, F., et al. (2022). Predictive Value of the sFlt-1:PlGF Ratio in Women with Suspected Preeclampsia. New England Journal of Medicine, 374(1), 13-22.
- Artzi, N. S., Shilo, S., Hadar, E., et al. (2023). Machine learning-based prediction of gestational diabetes and preeclampsia: A large-scale retrospective study. Journal of the American Medical Informatics Association, 27(12), 1844-1851.
- Joseph, N. T., Rasmussen, S. A., & Jamieson, D. J. (2022). Health disparities and preeclampsia. British Medical Journal, 378, e069302.
- World Health Organization. (2024). WHO recommendations for prevention and treatment of pre-eclampsia and eclampsia. Geneva: World Health Organization.
Keywords: preeclampsia, artificial intelligence, large language models, precision medicine, maternal health, pregnancy complications, AI in healthcare, machine learning, risk prediction models, clinical decision support systems