Women’s health and artificial intelligence: opportunities, evidence, risks and the path forward
Artificial intelligence (AI) is rapidly moving from an experimental technology into a practical component of healthcare.
For women’s health, this transition is particularly significant. Women have historically been underrepresented in biomedical research, women-specific diseases have received inadequate investment, and many conditions, including endometriosis, menopause-related disorders, autoimmune diseases, cardiovascular disease, infertility and pregnancy complications, remain poorly diagnosed or treated.
The convergence of AI, digital health, wearable sensors, medical imaging, electronic health records and large-scale biological datasets therefore presents an unusual opportunity: AI could help correct some of the information gaps that medicine has historically created around women. But if the underlying data are biased, AI can also automate and amplify those same inequalities.
Recent evidence suggests that the most important focus is on how to ensure that AI is clinically validated, representative, privacy-preserving and genuinely improves outcomes for women across different ages, ethnicities, socioeconomic groups and geographic settings.
Why women’s health needs better data
The case for applying AI to women’s health begins with a longstanding evidence gap.
A 2024 analysis of the women’s health gap estimated that addressing shortcomings in women’s health could reduce the amount of time women spend in poor health by almost two-thirds and potentially enable approximately 3.9 billion women to live healthier lives. The same analysis estimated a potential economic benefit of at least $1 trillion annually by 2040 from improvements in women’s health.
Yet important areas of women’s health continue to be under-researched. In 2025, Nature highlighted the continuing problem of studies that do not adequately consider sex and gender representation, calling for substantially more research focused specifically on women’s cardiovascular health, reproduction, mental health and public health.
The funding gap is also substantial. Reporting in 2025 on women’s-health research found that only around 1% of pharmaceutical research funding outside cancer went toward women’s health in 2024. This prompted major philanthropic initiatives, including a $100 million Pivotal–Wellcome Leap partnership designed to accelerate research into diseases that disproportionately affect women.
This matters for AI because algorithms learn from existing evidence. If women are missing from datasets, if female-specific diseases are poorly characterized, or if clinical records contain years of diagnostic delay, an AI system does not automatically correct those problems. AI cannot create high-quality evidence from systematically poor evidence.
The scale of AI in medicine has changed dramatically
AI-enabled medical devices have expanded rapidly. A 2025 analysis of the U.S. Food and Drug Administration’s database identified 1,016 AI/ML-enabled medical-device authorizations by December 20, 2024. Radiology accounted for the overwhelming majority.
Another 2025 analysis reported approximately 950 FDA-authorized AI/ML devices, with 723, about 76% in radiology.
The precise number depends on the date and methodology used to count authorizations, but the trend is unmistakable: AI has become an established part of medical-device development rather than a purely futuristic technology.
This has direct implications for women’s health because some of the most promising applications involve medical imaging, including breast imaging, ovarian imaging, gynecological ultrasound and pathology.
The FDA itself now maintains an AI-enabled medical-device list to improve transparency around authorized products and help clinicians and developers understand the evolving regulatory landscape.
AI and cancer care
Cancer is one of the most advanced areas for applying AI to women’s health. AI can analyse medical images, pathology slides, clinical histories and molecular information at a scale that would be difficult for individual clinicians.
Breast cancer: Mammography is particularly suited to machine-learning approaches because screening produces large quantities of standardized images.
AI can potentially identify suspicious lesions, prioritize high-risk examinations, assist radiologists with interpretation, detect subtle patterns that may be difficult to perceive, reduce diagnostic workload, support risk prediction and help personalize treatment.
The broader AI-and-cancer literature has increasingly shifted toward early detection, precision diagnosis and prediction of treatment response, rather than simply automated image classification. The 2025 Lancet Digital Health discussion of AI in women’s cancers described applications ranging from digital phenotyping and early detection to diagnosis and treatment.
Ovarian cancer: Ovarian cancer illustrates another potentially important application. In a 2024 study, researchers developed OvcaFinder, an interpretable deep-learning model combining ultrasound images, clinical information and radiologist assessments. The model reported an area under the ROC curve (AUC) of 0.978 in an internal test set and 0.947 in an external test set.
These numbers are encouraging, but they should not be interpreted as evidence that AI has “solved” ovarian cancer diagnosis. Performance in retrospective datasets does not automatically translate into improved survival or diagnostic accuracy in ordinary clinical environments.
The critical next step is prospective, multi-centre validation involving women from different populations.
Endometriosis: an important example of AI addressing diagnostic delay
Endometriosis is a particularly compelling case for AI because diagnosis can be difficult and delayed. AI researchers are exploring whether machine learning can identify patterns in: MRI, ultrasound, patient-reported symptoms, menstrual history, electronic medical records; and combinations of clinical and imaging variables.
A 2025 review of gynecological and obstetric emergencies reported evidence that AI analysis of MRI images can improve detection of ovarian endometriosis. Other research has investigated machine learning using self-reported patient data for earlier identification of endometriosis.
This could be important because AI offers something traditional healthcare systems often lack: the ability to integrate many weak signals simultaneously.
For example, individually, pelvic pain, painful menstruation, infertility and gastrointestinal symptoms may not appear sufficiently specific. An algorithm could potentially recognize a combination that signals elevated probability of endometriosis.
But this also illustrates a major limitation: an algorithm can reproduce the diagnostic assumptions embedded in its training data. If clinicians historically underdiagnosed women from certain racial, ethnic or socioeconomic groups, the AI system may learn that pattern rather than correct it.
Fertility and IVF
Fertility medicine is another rapidly developing area.
AI is being investigated for: embryo selection, ovarian-reserve assessment, prediction of IVF outcomes, timing of embryo transfer, sperm assessment, individualized stimulation protocols; and prediction of pregnancy probability.
A 2025 study developed an AI framework using routine 2D ultrasound to standardize assessment of antral follicles in assisted reproductive technology. The study included 395 women, divided into training, internal-testing and external-testing groups. AI is also being explored for personalized assisted reproductive treatment and predicting optimal embryo-transfer timing.
However, fertility AI presents a particularly important ethical challenge: what counts as success?
An algorithm optimized only for pregnancy rates could potentially ignore: maternal health, multiple pregnancy, miscarriage, long-term child health, patient preferences, financial burden; and psychological wellbeing.
The WHO has specifically warned that AI trained on reproductive-health datasets can produce skewed outputs, including in applications involving IVF.
Therefore, fertility AI should optimize for meaningful clinical outcomes rather than simply maximizing a statistical prediction score.
Pregnancy and maternal health
Perhaps the most consequential application of AI is maternal healthcare. The latest WHO estimates, published in 2025, show that approximately 260,000 women died during pregnancy or childbirth in 2023. More than 700 women died every day from preventable pregnancy-related causes, approximately one death every two minutes. Around 92% of maternal deaths occurred in low- and lower-middle-income countries.
The global maternal mortality ratio was approximately 197 deaths per 100,000 live births in 2023, compared with the Sustainable Development Goal target of fewer than 70 by 2030.
AI could potentially assist with: identifying high-risk pregnancies; predicting pre-eclampsia; detecting fetal abnormalities; interpreting ultrasound; triaging patients; supporting clinical decision-making; monitoring blood pressure and glucose; identifying deterioration; improving continuity of medical records; and extending specialist expertise into underserved areas.
This is particularly important in countries where specialist obstetric care is scarce.
A 2026 case study from Pakistan described Awaaz-e-Sehat, a speech-based AI system designed to generate electronic medical records and support antenatal care. The system evolved into a WhatsApp-based platform allowing women to create structured clinical information, receive AI-generated antenatal guidance and share records with healthcare providers.
This type of application illustrates an important principle: the most valuable AI in low-resource settings may not be a sophisticated diagnostic robot. It may be a tool that helps a woman communicate with the healthcare system, maintain her records and receive timely guidance.
AI could be especially valuable where specialists are scarce
The global distribution of healthcare professionals is profoundly unequal. For women in rural communities, conflict settings and low-income countries, the problem is often not that medicine lacks sophisticated knowledge. The problem is that the knowledge is unavailable locally. AI-assisted ultrasound, telemedicine, automated interpretation and decision-support systems could potentially bring elements of specialist expertise closer to patients.
The opportunity is especially significant for: obstetric ultrasound; cervical-cancer screening; breast imaging; fetal monitoring; triage; maternal-risk assessment; contraception counselling; and health education.
A 2026 Lancet Regional Health analysis of AI-assisted cervical-cancer screening cautioned, however, that much of the evidence still comes from pilot projects rather than large-scale health-system implementation.
That distinction is critical. A technology working in a controlled research environment is not the same thing as a technology improving women’s health at population level.
Sexual and reproductive health information
Generative AI has created a new pathway through which women can obtain health information. Women may ask AI systems about contraception, menstruation, pregnancy, sexually transmitted infections, fertility, abortion, sexual health, menopause and symptoms they are embarrassed or uncomfortable discussing with another person.
A 2024 report described a pilot in India in which women used an AI chatbot developed by the Myna Mahila Foundation to ask private sexual-health questions. The project illustrates one of the potential advantages of conversational AI: privacy and accessibility can make it easier for women to ask questions they might otherwise avoid asking.
A 2025 scoping review of AI in sexual and reproductive health searched more than 12,000 records and ultimately included 2,666 studies for its analysis, demonstrating how rapidly this research field has expanded.
Nevertheless, AI-generated medical information can be wrong, outdated or inappropriate for an individual’s circumstances. The correct role of generative AI should therefore generally be education and navigation, not autonomous diagnosis or treatment.
Menopause and chronic women’s health
The 2024 women’s-health gap analysis emphasized that conditions such as menopause and endometriosis can significantly affect quality of life and women’s ability to work. Approximately 80% of women experiencing menopause-related symptoms in the cited analysis reported that menopause interfered with their lives, with about one-third reporting depression.
AI could support personalized care by combining symptom diaries, sleep data, heart-rate information, physical activity, hormonal information, medication histories, laboratory results and patient-reported outcomes.
Digital “twins” are an emerging example. In 2024, one metabolic-care system described creating individualized models using more than 3,000 daily data points from connected sensors, including glucose monitors and wearable devices. Such systems can potentially account for differences in metabolic responses associated with menstrual cycles.
The scientific challenge, however, is determining whether these highly personalized systems actually improve long-term clinical outcomes.
AI and women’s cardiovascular health
Cardiovascular disease is particularly important because it has often been perceived as a predominantly male health problem.
Women can experience cardiovascular disease differently, and conventional risk models may not always capture sex-specific patterns adequately.
AI offers the possibility of incorporating a much wider range of variables into risk prediction.
A 2025 Lancet Digital Health study investigated an AI approach to cardiovascular-risk assessment in women and emphasized the continuing challenges of assessing cardiovascular risk and implementing prevention among females.
Potential applications include: predicting cardiovascular events, interpreting ECGs, detecting subtle imaging abnormalities, identifying high-risk pregnancy-related cardiovascular profiles, incorporating reproductive history into risk models and personalizing prevention.
Pregnancy itself can provide important information about later cardiovascular health. Conditions such as pre-eclampsia and gestational diabetes may signal increased future cardiovascular risk. AI could potentially connect these life-stage data rather than treating pregnancy as an isolated episode.
Wearables and continuous monitoring
Wearable technologies are transforming the quantity of health information available about women.
Devices can collect heart rate, sleep, physical activity, temperature, glucose, menstrual-cycle information, blood pressure and other physiological signals.
AI can then identify patterns across these continuous data streams. The 2025 Nature Communications review of biomonitoring technologies highlighted applications across breast and gynecological cancers, vaginal infections, fertility, pregnancy and post-menopausal osteoporosis.
This represents a shift from episodic healthcare to continuous health monitoring. Traditional medicine might measure a woman’s blood pressure once every few months. A wearable system could potentially identify changes occurring over weeks or months. But more data are not necessarily better data. Continuous monitoring can generate false alarms, anxiety and unnecessary clinical interventions.
AI could help close the women’s health data gap, but only if women are represented in datasets
This is perhaps the most important paradox. AI is often described as a tool that eliminates human bias. In reality, AI can inherit bias from its training data. Suppose a diagnostic dataset contains: 80% men, 20% women, predominantly high-income patients, limited representation of African populations, few older women and very few pregnant women.
An algorithm trained on that dataset may perform extremely well overall while performing substantially worse for a subgroup.
A high overall accuracy can therefore conceal serious inequity. The WHO has repeatedly emphasized that AI systems can encode existing biases involving sex, gender, race, ethnicity, age and other characteristics.
The 2026 WHO Europe report on digital-health equity similarly warned that AI benefits can be distributed unevenly and noted that many regulatory frameworks pay more attention to ethnicity and gender than to factors such as language, income, geographic location and disability.
For women’s health, this means datasets should be deliberately designed to include women across: age groups, ethnicities, geographic regions, socioeconomic backgrounds, pregnancy status, reproductive stages, menopause status, disability status and different healthcare systems.
The business opportunity is expanding
Women’s health is also becoming an increasingly important investment sector.
A 2026 Silicon Valley Bank analysis reported approximately $2 billion in venture-capital investment in women’s health in 2025 and found that AI-enabled women’s-health companies commanded median pre-money valuations of approximately $35 million, nearly three times the sector median.
The same report suggested that women’s-health AI investment is increasingly concentrated on clinical tools designed to predict and prevent costly health events.
This investment is potentially beneficial because commercialization can accelerate research and product development.
But commercialization creates its own risks. An app that earns money from subscriptions or advertising may have incentives that do not perfectly align with a patient’s health interests.
The fundamental question should therefore be: Does the business model reward better health outcomes, or simply more data and more engagement?
Conclusion
Artificial intelligence has moved decisively into women’s healthcare. The technology is being investigated and deployed across breast and gynecological cancer, endometriosis, fertility, pregnancy, cardiovascular disease, sexual and reproductive health, menopause and continuous physiological monitoring.
The opportunity is enormous because women’s health has an equally enormous evidence gap.
Global maternal mortality remains unacceptably high, with about 260,000 maternal deaths estimated in 2023. At the same time, more than 1,000 AI-enabled medical devices had already received FDA authorization by late 2024, illustrating how quickly medical AI is becoming part of clinical infrastructure.
The most promising future is not an AI that “replaces the doctor.” It is an AI system that helps clinicians detect disease earlier, gives women better access to understandable information, connects fragmented health records, identifies people at elevated risk and makes women’s biology more visible in medical research.
But AI will only close the women’s health gap if it is deliberately designed to do so. The central principle should be simple: use AI to build a healthcare system that finally has enough data, evidence and attention to understand women properly.
That means investing simultaneously in women’s health research, representative datasets, clinical validation, privacy, regulation, digital infrastructure and women’s participation in AI design. If these conditions are met, AI could become one of the most powerful tools of the next decade for improving women’s health.