How Medical Research Is Failing Women – And How to Fix It

For decades, medical research has operated under a dangerous assumption: that what works for men will work just as well for women. This gender data gap—the systemic lack of research focused on women’s health—has led to misdiagnoses, ineffective treatments, and worse health outcomes for women. Women’s symptoms are often dismissed or misunderstood, drug dosages are based on male physiology, and conditions such as heart disease and autoimmune disorders are frequently overlooked.

Artificial intelligence (AI) is now emerging as a potential tool to close this gap by identifying overlooked patterns in women’s health. However, if AI is trained on biased data, it could also reinforce the very inequalities it aims to solve.

Why Women Are Left Out of Medical Research

There are two main reasons for the gender data gap in healthcare. First, diseases that predominantly affect women—such as endometriosis, menopause-related conditions, and autoimmune disorders—are severely underfunded and under-researched. Historically, women were excluded from clinical trials due to concerns about hormonal fluctuations affecting study results. Though these policies have changed, their impact persists in medical research today.

Second, medical research is often conducted from a male-centric perspective. Many studies still fail to analyse data separately for men and women, assuming that findings from male subjects will automatically apply to women.

This has serious consequences:

• Women are 50% more likely to be misdiagnosed during a heart attack because their symptoms—nausea, shortness of breath, and jaw pain—differ from the chest pain typically seen in men.

• ADHD in women is frequently overlooked because symptoms manifest differently than in men, leading to misdiagnoses or missed diagnoses altogether.

• Women’s chronic pain is often dismissed as psychological rather than physical, contributing to inadequate pain management. Conditions like fibromyalgia and endometriosis, which cause debilitating pain, can take years to diagnose—seven years on average for endometriosis.

  
  

Additionally, many countries do not collect or report health data separately by sex, making it difficult to track gender disparities in disease prevalence, treatment effectiveness, and healthcare access.

The Role of AI in Closing (or Widening) the Gap

AI has the potential to transform healthcare by analysing vast amounts of medical data and identifying patterns that traditional research has missed. Machine learning models are already improving diagnostic accuracy for breast cancer, and similar tools could be used to detect subtle differences in symptoms between men and women, reducing misdiagnoses and delays in care.

AI can also support personalised medicine by tailoring treatments to an individual’s genetic makeup, hormonal profile, and medical history. This approach could revolutionise how conditions like menopause, cardiovascular disease, and reproductive disorders are treated.

However, AI is only as good as the data it is trained on. If algorithms are built using biased medical data—historically centred on male patients—they will continue to overlook women’s health concerns. A recent study by University College London found that AI screening tools for liver disease were nearly twice as likely to miss diagnoses in women compared to men, simply because the AI models relied on biochemical markers that are more effective indicators of liver disease in men.

To ensure AI benefits women’s health rather than reinforcing existing inequalities, developers must:

• Train AI models on diverse datasets that include gender-specific health factors.

• Implement sex-disaggregated analysis to identify differences in disease presentation and treatment response.

• Introduce transparency measures to prevent algorithms from perpetuating biases.

  
  

What Needs to Change

Closing the gender data gap requires systemic changes in how medical research is conducted, funded, and applied.

Key actions include:

1. Inclusive Research Practices: Women must be equally represented in clinical trials, and studies should analyse how diseases and treatments affect them differently.

2. More Funding for Women’s Health: Research into conditions that disproportionately affect women needs substantial investment.

3. Sex-Specific Data Collection: Health data should be collected and reported separately for men and women to highlight disparities and improve treatment effectiveness.

4. Medical Training Reform: Healthcare professionals need better education on gender differences in disease symptoms, diagnosis, and treatment.

   
   

A Future of Equitable Healthcare

For too long, women’s health has been treated as an afterthought in medical research. But with the right investments, policies, and use of emerging technologies like AI, we can close the gender data gap and ensure women receive the care they deserve.

The future of healthcare must be designed with women in mind—not as an afterthought, but as a priority.