The digital age promises revolutionary advancements in healthcare through AI, but we are facing a critical challenge: a glaring void in women’s health data.
This video uncovers the profound “Gender Data Health Gap”—the difference in quality and quantity of health data collected and analysed between women and men.
Why is women’s health data missing?
• Historical exclusion: Until 1993, women were not mandatorily included in US clinical research. Even 30 years later, there’s a significant 17-year lag for this research to reach patient care.
• Ongoing biases: A majority of biomedical research continues to predominantly rely on male mice.
• Underrepresentation: Women’s participation in clinical trials, especially early phases, remains low, and pregnant/lactating women are often excluded entirely. Studies often don’t report sex distribution among participants or provide sex-disaggregated effectiveness estimates.
The alarming implications of this gap:
• Misdiagnosis and Suboptimal Treatments: Current diagnostic and treatment paradigms are largely based on male-centric data, leading to women waiting an average of 4 years longer for a diagnosis for the same disease as men. Women are less likely to receive appropriate pain medication, yet more likely to be prescribed anti-anxiety drugs like benzodiazepines.
• Patient Dismissal: A staggering 84% of women report feeling dismissed by their GP in the UK, and nearly 1 in 4 women feel their pain isn’t taken seriously by clinicians.
• Limited Treatment Options: There are only 2 FDA-approved treatments for female sexual dysfunction (impacting ~40% of women) compared to 27 for men.
• AI’s Magnifying Effect: Existing AI models are trained on these limited and biased datasets. The rapid adoption of AI without rectifying these foundational gaps threatens to perpetuate and even amplify these disparities at an unprecedented scale, making women “invisible” or misrepresented in data. This leads to algorithmic bias, where AI compounds existing inequities.
How bias enters AI in healthcare:
Bias can be inherent, stem from unrepresentative sampling (e.g., genomics research focusing on European males), data proxies, generalisation of models, or flawed evaluation.
Bridging the Gap:
The Path Forward We need a multifaceted approach.
While collecting new real-world data is crucial, strategies include:
• Acknowledging limitations: AI solutions must clearly communicate data biases.
• Collaboration: Working with gender researchers and clinicians. • Crowdsourcing & Citizen Science: Engaging women to contribute health data, for example, through wearables or apps like Clue.
• FemTech’s Role: Startups like Impli, Daye, and TheBlood are uniquely poised to collect novel, previously neglected datasets and reimagine care.
• Ethical Oversight: Establishing ethics committees for AI in healthcare.
• Policy Change: Advocating for more inclusive and diverse data collection in future clinical trials.
Learn more about the urgent need to redesign healthcare for inclusivity and ensure women’s health is not relegated to the periphery!
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