How is Women’s Health Benefiting from AI?

AI as a Tool for Screening and Diagnosing Underexplored Female Diseases

Today in France, it takes an average of seven years to diagnose endometriosis. In response to this medical need, several players are leveraging AI to accelerate this diagnosis:

• The Robinson Research Institute and the Australian Institute for Machine Learning are developing a tool that combines the results of transvaginal ultrasounds and MRIs to diagnose endometriosis.
• The company Ziwig combines AI and genomics to diagnose endometriosis long before clinically recognizable symptoms appear.

More generally, AI often saves time in disease screening and diagnosis, increasing the chances of early treatment and improving life expectancy and quality of life. AI’s contributions in this phase are often linked to reading medical images and assisting in result interpretation:

• In breast cancer screening, AI-assisted mammography analysis can reduce reading time by 13%. This results in faster diagnosis for positive cases and a higher number of exams processed per day, which is crucial given that early breast cancer detection ensures a 99% survival rate five years post-diagnosis.
• In digital cytology (cell analysis using digital images) for cervical cancer screening, AI rapidly assesses tens of thousands of cells from a patient’s sample, selecting the most relevant images for healthcare professionals to support their diagnosis.

AI for Providing Personalized Care for Women

Although men and women do not always present the same symptoms or respond to treatments in the same way, gender differences are often overlooked in therapeutic management.

By analyzing genetic, age-related, weight-related, reproductive status, and medical history data—available through electronic health records and connected devices—AI can help personalize care, ultimately improving patient management.

For example, the startup Tempus has developed AI algorithms to predict how patients will respond to various treatments (notably for breast cancer), helping tailor therapies to maximize their effectiveness.

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AI for Improving the Prevention of Physical and Psychological Risks

42% of women do not undergo heart screenings, despite cardiovascular diseases being the leading cause of death among women. Additionally, many diagnostic indicators are based on models developed exclusively for men. Any tool that increases the number of women undergoing specific screenings would be beneficial.

This is the objective of Cardio Diagnostics, which offers a three-year AI-based coronary heart disease risk assessment test with a sensitivity of 78% for women and 76% for men. These figures are particularly relevant as the test is based on molecular analysis rather than perceived physical symptoms, which differ between men and women.

Another example: AI can assist healthcare professionals in identifying pregnant women at high risk of complications, such as preeclampsia, gestational diabetes, or preterm labor, by analyzing electronic health records and data from connected health devices.

Finally, in mental health, AI could enable early detection of postpartum depression (affecting 15% of women) or psychological distress linked to heavy treatments. An example is Mika Health, a platform that supports breast cancer patients. This technology could allow for rapid intervention, ensuring better support for mothers and patients.

AI for Enhancing Women’s Representation in Clinical Trials: Participation and Result Relevance

Clinical trials on digoxin (a drug for heart failure) were conducted with a study group comprising 80% men. The results led to a general recommendation of digoxin for all patients. However, years later, a post-hoc analysis revealed that women taking digoxin had a higher mortality rate compared to those taking a placebo. Since women made up only 20% of the initial study, this effect had gone unnoticed. Increasing female representation in clinical trials is a real issue.

AI can play a role here as well: by rapidly filtering patient profiles while considering diversity, AI can help ensure better representation in clinical trials, particularly for women. This includes detecting and eliminating potential biases in study design and using predictive modeling to identify groups that may face entry barriers, allowing researchers to implement strategies to overcome them.

Additionally, AI accelerates data processing, making it easier for researchers to segment results by categories—including gender—which was not always done before. This gender-based segmentation helps identify potential side effects that are more prevalent in women than in men, verify the tested product’s efficacy across sexes, and improve knowledge about both female and male physiology.

Artificial intelligence enables significant advancements in healthcare, particularly in women’s health. It addresses specific challenges by assisting in screening and diagnosis, facilitating personalized care, preventing physical and psychological risks, and improving representation in clinical trials. The applications and examples presented are not an exhaustive list of all possibilities, and it is important to remember that AI remains a support tool for medical professionals.

More broadly, the integration of AI into healthcare practices raises important questions: How can we ensure data security? How can we guarantee interoperability between AI-powered devices and medical IT systems? How should the role of healthcare professionals be balanced with AI technology?

Alcimed can support you in exploring AI applications in healthcare pathways and drug development, as well as preparing for their market integration. Don’t hesitate to contact our team!

About the Author

Elia, Consultant in the Healthcare team at Alcimed France.