AI in Women’s Health – Reshaping the Future as We Know It

In recent years, artificial intelligence (AI) has emerged as a powerful force across various industries – and healthcare is no exception. No matter what your role is within the healthcare sector it is important to understand how AI is starting to shape the future of health, from improving diagnostic accuracy to streamlining hospital operations and creating personalised patient treatment plans. 

Understanding how AI is integrating into our healthcare system isn’t just timely – it is essential. 

This article explores four key ways in which AI is helping to advance women’s health: improving screening and diagnostic accuracy, enabling more personalised care, supporting preventative strategies, and enhancing the quality and inclusivity of clinical trials. 

How is Women’s Health Benefiting from AI

Women’s health has historically received less attention in medical research, many gaps still exist today and there is a growing need to tailor treatments to better suit women’s unique health needs. AI technology is increasingly helping speed up disease screening and diagnosis – leading to better outcomes and longer, healthier lives. 

A major hurdle lies in expanding our understanding of female-specific biological processes – such as menstruation and menopause and addressing conditions like endometriosis that remain underdiagnosed and undertreated.

As an example, despite affecting over 10% of women globally, endometriosis takes an average of 6.6 years to be diagnosed worldwide. Researchers at the Robinson Research Institute and the Australian Institute for Machine Learning are developing a tool that uses a combination of results from transvaginal ultrasounds and MRIs to formulate a diagnosis of the disease.

The French biotech company Ziwig, who pride themselves on innovating women’s health – combine AI and genomics to diagnose endometriosis long before clinically recognisable symptoms appear. 

Another major way in which AI is contributing is by analysing medical images and assisting in result interpretation. Studies have shown that in breast cancer screening, AI-assisted analysis of mammograms can cut down the time it takes to review images by around 13%. This means that more patients can be seen each day, and those with positive results can be diagnosed and treated faster – a critical factor since early detection boosts the five-year survival rate to about 99%.

In cervical cancer screening, digital cytology can now rapidly access tens of thousands of cells from a single patient sample. With AI systems highlighting the most relevant images, healthcare professionals are supported in both speed and accuracy in their diagnosis.

AI for Providing Personalised Care for Women

Gender-specific differences are still often ignored when it comes to planning care and treatment, despite it being widely known that men and women can experience different symptoms and may react differently to the exact same treatments. 

By making use of detailed health information such as genetics, age, body weight, reproductive history, and medical background all gathered from electronic health records and smart health devices, digital tools can help create more tailored treatment plans that better suit each individual and notably, acknowledge the necessity of providing gender-specific treatment. 

A good example is the health tech company Tempus, which uses analysis of patient data to predict how someone might respond to certain treatments, particularly in breast cancer. This helps healthcare professionals choose the most effective treatment from the beginning, improving care and outcomes.

Using Technology to Help Prevent Health Risks in Women

Data has shown that many women miss out on important health screenings. As an example, 42% of women globally do not undergo heart screenings despite cardiovascular disease being the top cause of death among women worldwide. Additionally, many diagnostic tools are based exclusively on men, making it harder to detect risks in women. Therefore, expanding resources to encourage greater participation of women in targeted screenings would be highly beneficial. 

The American company Cardio Diagnostics aims to change this with a three-year heart disease risk test that uses molecular data instead of physical symptoms, which are known to show up differently in both genders. The test has shown strong accuracy rates for men and women with a sensitivity rate of 76% and 78% respectively. 

AI is also playing a growing role in pregnancy care. By reviewing health records and wearable device data, digital tools can help spot early warning signs for complications in women with high risk of complications such as preeclampsia, gestational diabetes, or preterm labour.

Mental health support is another key area. Tools like Mika Health offer emotional support to women undergoing intense treatments, such as breast cancer treatment, and can help detect signs of postpartum depression early – allowing for quicker, more effective support and aid recovery. 

Improving Women’s Representation in Clinical Trials Through Technology

In the past, some clinical trials have heavily underrepresented women. For example, early studies on the heart medication Digoxin (a drug for heart failure) consisted of 80% men. The results led to a general recommendation of the drug, but follow-up analysis showed that women on digoxin had a higher rate of mortality than those who took a placebo. A critical finding missed because women made up too small a portion of the original study.

AI can help address this imbalance. By quickly analysing patient data with diversity in mind, advanced tools can support more balanced trial recruitment. AI technology can also flag potential biases in how studies are designed and predict which groups might struggle to participate – helping researchers make studies more inclusive from the start.

Conclusion – AI in Women’s Health Care

Artificial intelligence is transforming healthcare by offering new ways to address the specific health needs of women. It plays a vital role in enhancing early diagnosis, enabling more personalised treatment plans, promoting preventive care, and making clinical trials more representative. While these advancements are promising, they represent just the beginning.

It is also important to recognise that many AI tools are still in development and not yet widely used in everyday clinical settings. As innovation continues, healthcare systems must adapt thoughtfully and responsibly.

AI is designed to support and not replace healthcare professionals. Any AI-generated insights must always be interpreted by trained clinicians. To ensure patient safety, it is crucial that ethical guidelines are in place and that medical practitioners have training in AI and how it contributes to clinical decisions, preventing overdependence and complacency.

At the same time, the growing use of AI in medicine brings important questions to the table: How can we protect patient data? How do we make sure different systems and devices work well together? And most importantly, how do we strike the right balance between cutting-edge technology and the human expertise at the heart of healthcare?