How AI is Transforming Women's Healthcare

Artificial intelligence is transforming and reframing various aspects of healthcare through better diagnosis and personalization of treatments at a faster pace than technologies before it. In particular, AI serves promise for underserved areas of healthcare, like women's health, which has long been affected by a lack of research and data gaps.

AI for women's health can transform the diagnosis and treatment of female-specific and general conditions that affect women in specific ways. It can help improve gaps in research and support women through various steps in their health journey.

In this article, we paint the picture of how AI-powered women's health solutions are already transforming women's lives.

Overview of gaps in women's healthcare

Before discussing practical ways of improving female health with AI, here is a brief overview of the gender-specific issues that women face in healthcare:

• Underfunded research

Only 1% of healthcare research funding is dedicated to female-specific conditions, including women's cancers. Women were not always included in clinical trials in the USA until 1993 when Congress passed the law that demanded it. This was delaying progress in understanding female-only diseases and the ways in which general diseases affect women.

• Pain disregard

Women's pain is often dismissed or attributed to psychological factors, delaying diagnoses for gynecological conditions like endometriosis and autoimmune diseases. Women wait 33% longer than men for pain treatment in ERs (Source ). Endometriosis diagnosis takes 7–10 years on average due to symptom dismissal.

• Lack of research on hormonal disorders

Conditions like polycystic ovary syndrome (PCOS), endometriosis, and thyroid disorders are often misdiagnosed or overlooked. 1 in 10 women have PCOS, yet 50% remain undiagnosed (Source ). Menopause-related issues receive little medical attention despite affecting over 1 billion women globally by 2025.

• Poor recognition of women's cardiology needs

Heart disease symptoms in women differ from those in men, leading to underdiagnosis and inadequate treatment. Women are 50% more likely to be misdiagnosed after a heart attack than men (Source ).

Diseases that disproportionately affect women:

• Osteoporosis – 80% of cases occur in women, yet screening rates remain low (Source ).
• Lung cancer – rising in non-smoking women, with cases increasing by 79% in the last four decades (Source ).
• Autoimmune diseases – 78% of cases affect women, with the lack of research into gender-specific causes is still limited (Source ).

The role of technology in addressing gender disparities in healthcare

Addressing gender disparities in healthcare requires a multifaceted approach, with technology playing a pivotal role in bridging these gaps. Two critical areas where technology is making significant strides are empowering women to manage their health data and mitigating biases in artificial intelligence in healthcare market applications.

The emergence of FemTech —technology designed to address women's health needs—has led to the development of AI-driven, patient-centric tools that empower women to manage their health actively.

• Personalized health insights

Applications like March Health leverage advanced AI to provide personalized care and support for women's health, improving outcomes for conditions like endometriosis and PCOS.

• Mitigating gender bias in AI applications

While AI has the potential to revolutionize healthcare, it can also perpetuate existing biases if not carefully managed. Identifying and mitigating these biases is crucial for developing equitable AI applications.

• Bias identification and reduction

Addressing gender disparities in healthcare is crucial for achieving equitable health outcomes. Women have historically faced challenges such as underrepresentation in medical research, misdiagnosis, and limited access to specialized care. Advancements in technology, particularly in artificial intelligence (AI), are pivotal in bridging these gaps. AI-driven data collection and analysis tools are enabling more inclusive datasets.

• Inclusive data collection

Ensuring that AI models are trained on diverse and representative datasets is essential. This involves including data from women of different ages, ethnicities, and socioeconomic backgrounds to prevent reinforcing existing biases.

AI's role in transforming women's healthcare

What does AI in women's healthcare offer to bridge the gaps in tackling women-specific issues in diagnostics and treatment? Our overview looks into specific technologies, both implemented and currently in research.

Identifying adverse drug reactions

Women are twice as likely as men to have adverse drug reactions, which are the fourth most common cause of death in the US. Some of these reactions remain vastly understudied.

Currently, scientists are using machine learning and AI to predict sex differences in drug responses with the help of pharmacogenomic data.

Scientists can identify tens of thousands of adverse drug responses through pharmacovigilance algorithms that can also analyze the drug reactions retrospectively through clinical trials and electronic health records (EHRs) (Source ).

Enhancing diagnostic accuracy for female-specific cancers

AI in diagnostics for women significantly improves the detection of diseases that disproportionately affect them. In mammography, AI enhances breast cancer screening accuracy.

A study in Germany found that AI-assisted screening increased breast cancer detection rates from 5.7% to 6.7%, identifying one additional case per 1,000 women screened without increasing false positives (Source ).

Studies that involve AI in diagnostics for women are now common and are progressing rapidly.

Some of these studies are large and have the potential to become very impactful. For example, The UK's National Health Service (NHS) is conducting a substantial trial involving approximately 700,000 women to assess the efficacy of AI in breast cancer screening.

The AI technology compares new mammograms to a vast database of previous scans, aiming to identify abnormalities indicative of cancer. This approach can double a radiologist's efficiency and improve early-stage cancer detection. If successful, the program may expand nationwide (Source ).

The study could increase the effectiveness of scanning, especially when the diagnosis is not apparent through the first scan, and you have to seek a second or third opinion from a different radiologist.

Some of the research is focused on detecting breast cancer years before it develops. For instance, researchers at MIT and Massachusetts General Hospital developed a deep-learning model capable of predicting the likelihood of a patient developing breast cancer up to five years in advance by analyzing mammogram images (Source ).

The images associated with these studies often illustrate how AI can highlight areas of concern on mammograms that appear normal to human observers, effectively identifying potential cancers well before they become detectable through traditional methods.

In this study, these AI solutions for women's health can identify high-risk patients more accurately than conventional diagnostics and recommend different screening calendars.

Aside from breast cancer, there are studies targeting cancers in gynecology as well, which is especially significant for conditions that are more difficult to detect, like ovarian cancer.

Tackling underdiagnosis of PCOS and endometriosis

Conditions like endometriosis and polycystic ovary syndrome (PCOS) remain underdiagnosed due to stigma and symptom misinterpretation.

AI in women's health helps doctors recognize symptom patterns and detect these conditions earlier. ML algorithms analyze patient history and symptoms to identify cases that might otherwise go undiagnosed. AI-powered risk assessment tools improve early detection rates.

To illustrate this with specific examples, PCOS can now be diagnosed with machine learning algorithms from EHRs of people at risk (Source ). Full implementation of this technique requires more advanced studies with large-scale populations involved. AI-driven models like CystNet have also been designed to detect and classify PCOS from ultrasound images (Source ).

Studies have explored the use of deep learning methods to classify endometriosis using ultrasound data. Telehealth platforms like March Health can help identify potential endometriosis non-invasively by analyzing medical history.

Optimizing reproductive health through in-vitro fertilization (IVF)

AI in women's healthcare is transforming fertility treatments by increasing success rates in in-vitro fertilization (IVF). AI models analyze embryo quality to select the most viable embryos for implantation. The result is improving the pregnancy success rate among women seeking IVF.

In the UK, AI-driven embryo selection tools have helped increase IVF success rates by identifying the healthiest embryos for transfer (Source ). As of 2025, there are multiple programs across the globe that evaluate embryo viability during IVF procedures. These technologies analyze biological data to score embryos based on their probability of successful implantation.

Managing maternal health

AI for maternal health helps mitigate risks for women during and after pregnancy. For example, Cedars-Sinai Hospital uses AI-powered women's health solutions to identify women at risk of preeclampsia. Their other study showed that AI and machine learning algorithms can identify patients at risk of bleeding after childbirth (Source ). These findings already help to prepare for critical care of pregnant patients.

Other potential applications in pregnancy care that are considered at the hospital are personalized treatments for patients who develop gestational diabetes and hypertension during pregnancy. In this context, AI for women's health can help identify patients who need extra care beyond standard recommendations (personalized treatment) or urgent care.

AI for maternal health can also tackle complications related to the fetus's development. While it benefits all sorts of women, it is critical for those with compromised access to checkups during pregnancy.

In Uganda, AI-powered ultrasound technology is piloted to provide essential pregnancy scans in rural areas. The ScanNav FetalCheck software by Intelligent Ultrasound accurately dates pregnancies without the need for specialist sonographers, offering crucial insights into early pregnancy and potentially reducing stillbirths and complications. This technology, suitable for use by midwives or nurses, aims to engage women in antenatal care earlier and allows portable scanning at home (Source ).

AI assistants as a gamechanger for women's

One of the AI-powered women's health solutions that deserve special attention is AI assistants. While other technologies mainly guide doctors through intervention, AI assistants provide on-the-go health guidance for women without intermediaries. Healthcare chatbot development is a popular branch of AI development services .

AI assistants are available around the clock, so they can significantly help those with urgent non-life threatening concerns, like new mothers seeking guidance in the first few months of their child's life.

Many women lack easy access to gynecologists or maternal health specialists. AI assistants bridge this gap by offering virtual consultations and guided health education. For many women, AI assistants can be a first step toward a correct diagnosis by pointing them to the correct diagnosis and helping them find a competent specialist.

This is important in conditions like endometriosis, which take years to diagnose correctly. They also act as a simple checker, allowing women to decide whether they need further consultations with specialists. When the woman finally sees the doctor, she can use an AI bot as a health record assistant to maximize the information that will be shared with a physician.

Women can also have judgment-free conversations with virtual chatbots on topics like menopause management or sexual health if they have some reservations while talking to an actual specialist. Finally, AI chatbots can help save costs on non-essential doctor visits.

Here are some examples of AI female assistants:

• Ella by PatientsByMe provides personalized health recommendations and custom resources depending on the issue the woman is facing.
• Сlaire is a mental-health support chatbot that assists with managing stress and anxiety. Even though it is not a replacement for therapy, it can provide instant support when the therapist is not readily available.
• Menopause Maven is a menopause-support chat that is part of the platform YesChat.ai. It provides empathetic support to help users manage their menopause journey through mental health support and lifestyle changes.

Challenges in implementing AI for women's health

Implementing artificial intelligence for women's health faces challenges, including the ones that affect all healthcare technologies and those specific to data gaps in women's health .

• Data privacy and consent

Applications that use artificial intelligence in women's healthcare contain sensitive information about women's health, so some users are concerned about sharing data with these apps. Some health applications have been found to share user data without explicit consent. Transparent data policies and legal frameworks are key for AI in women's healthcare.

• Gender biases in AI models

AI systems trained on biased datasets can perpetuate existing gender disparities in healthcare, even though they are aimed at precisely the opposite. After all, medical research has historically underrepresented women, and the vast amount of the existing medical data is male-centric.

The creators of the technologies should prioritize incorporating data that reflects gender diversity to train AI models. Additionally, it is essential to regularly assess AI systems for potential biases and adjust algorithms to mitigate discriminatory outcomes.

• Limited availability of gender-specific health data

This brings us to our next issue: while prioritizing sex-specific data is key, lack of research leads to limited availability of some data and data gaps .

This issue requires comprehensive multi-actor solutions, such as promoting policies that prioritize women in healthcare research and investing in it. Apps can crowdsource anonymized health data to fill some gaps through user-generated data accumulating over time.

In addition, app developers should collaborate with hospitals, universities, and research labs to access validated datasets from clinical studies. Finally, developers must audit AI models for gender bias by testing algorithms on female-specific datasets.

Collaborating to drive change

Implementing AI solutions in women's healthcare requires collaboration with experienced tech talents to navigate the complexities and ensure effective outcomes. Binariks is a proficient partner in this domain, offering tailored services to meet the unique needs of healthcare providers.

Here is what we can do:

• AI-powered custom healthcare software development with machine learning models for personalized diagnostics and treatment recommendations.
• Ensure secure and compliant data handling by implementing HIPAA/GDPR-compliant architectures and advanced data encryption techniques.
• Implement bias detection using Fairness AI algorithms and real-time bias audits to improve diagnostic accuracy for female-specific conditions.
• Leverage cloud-based infrastructure for scalable AI solutions, enabling remote patient monitoring and real-time health analytics .
• Integrate AI-driven decision support systems into EHRs and telemedicine platforms for seamless clinical workflows.
• Build intuitive, secure, and HIPAA-compliant platforms with natural language processing (NLP) chatbots and other valuable features for women's health data.

AI is already transforming women's healthcare by offering more accurate diagnoses and treatments for previously overlooked conditions. Bridging data gaps with AI assistance help close persistent disparities in women's health. As AI continues to advance, we can expect earlier detection, more tailored treatments, and better outcomes for women everywhere.