AI in Femtech: Don’t Go with the Flow

Understudied. Underfunded. Underrepresented.

Women’s health has historically received less attention, with women underrepresented in clinical trials and sex-based differences often overlooked, contributing to disparities in diagnosis and treatment. Female technology (“femtech”) emerged as a response, creating tools tailored to women’s needs to support health management and greater agency over their bodies. As artificial intelligence becomes increasingly integrated into healthcare, it is expanding femtech’s capabilities to analyze health data and generate more individualized insights. Yet this integration creates a paradox: technologies intended to empower women may also disadvantage women by reinforcing existing biases and creating new risks to how their health information is collected and used. Ensuring that AI-driven femtech is developed and governed responsibly is therefore just as critical as the innovation itself.

Femtech and the Role of AI

Femtech is a sector of digital products and services designed for women’s health and wellness needs, enabling women to better understand and proactively manage their health. Examples include apps, wearables, software, diagnostic tools, and telehealth platforms that monitor menstruation, fertility, pregnancy, sexual wellness, menopause, chronic conditions, and mental health. Its growing adoption reflects increasing demand for convenient and personalized approaches to health management, with the global femtech market forecasted to grow from US$9.78 billion in 2026 to US$18.98 billion by 2031.

As femtech expands, so too does the volume of intimate health information integral to its products. This data may be provided by users or collected by digital devices, and includes reproductive and menstrual health records, biometric measurements, pregnancy-related information, sexual and contraceptive history, symptom tracking, and behavioural metrics. AI can analyze this data to identify patterns, produce individualized insights, and detect potential health concerns earlier. Accordingly, AI-driven femtech can complement traditional healthcare by providing more continuous and accessible forms of health management. However, AI may also infer sensitive information beyond what users directly provide—including reproductive choices, family planning, or other intimate aspects of personal life—raising broader questions about how such information is interpreted and used.

The Default Male in Healthcare

The “default male” in healthcare refers to the use of male physiology and experiences as the medical standard, despite sex-based differences in hormonal regulation, metabolism, immune function, and body composition. Thalidomide provides a notable example: it was prescribed for morning sickness, among other uses, without being tested in pregnant women, causing severe birth abnormalities and deaths. The resulting restrictions on women of childbearing age in clinical trials further limited their representation in research. Similarly, although women metabolized the sleep medication, Ambien, more slowly than men, initial dosing recommendations relied primarily on male data until the FDA halved the recommended dose for women in 2013. Overlooking these differences in women can affect the presentation of health conditions and the metabolism of medications, contributing to delayed diagnoses, inadequate treatment, and increased risks of adverse reactions. In fact, a 2025 McKinsey Health Institute report found that Canadian women spend 24% more time in poor health and disability than men, estimating that closing the gap could generate $37 billion annually by 2040.

The same pattern can extend to AI-driven femtech. Many AI tools use machine learning systems trained on existing health data to identify patterns and generate predictions. When training data does not adequately represent different populations, algorithms may be less effective in recognizing symptoms or assessing risk for certain groups. Structural inequalities also exist, with women underrepresented among founders, investors, and decision-makers in healthcare innovation. Limited input from women and healthcare providers during development, combined with pressure to reach market quickly, may result in insufficiently tested products. As AI systems learn from biased or incomplete datasets, they may carry these limitations into newer technologies, potentially reproducing existing disparities through algorithmic processes—even without intentional bias.

Bias can also arise from the design of healthcare technologies themselves. Wearables, for example, may incorporate assumptions about body size or physiology that reflect male norms (e.g. sensor placement, device configuration), affecting the accuracy of data collected from women and subsequent AI-derived outputs. Femtech products may similarly embed narrow definitions of “normal”, such as expected menstrual cycles or reproductive goals, potentially excluding or misrepresenting women whose experiences fall outside these assumptions.

Together, these examples illustrate how the data and design of AI-driven femtech can perpetuate inequities based on gender, race, ethnicity, socioeconomic status, or other related factors.

Responsible AI in Femtech

The central challenge facing AI in femtech is not whether these technologies should exist, but how they should be developed and governed. Companies creating AI-driven health technologies must ensure that innovation does not come at the expense of key ethical principles such as transparency, accountability, safety, and fairness.

Transparency is a key requirement. Users and healthcare providers should understand how AI systems generate recommendations, what limitations exist, and when human judgment remains necessary. Without transparency, individuals may place unwarranted confidence in automated outputs that contain hidden biases or inaccuracies.

Accountability is equally important. As AI systems increasingly inform health decisions, responsibility can become unclear when automated tools produce harmful outcomes. Effective governance requires standards for evaluating AI performance, monitoring potential harms, and determining responsibility when systems fail.

Safeguards should also be incorporated throughout development rather than introduced after problems occur. This includes using diverse training data and testing AI tools across varied populations to reduce the risk of biased or inaccurate outputs, as well as evaluating systems for potential harms before deployment. Involving women and healthcare professionals in design and evaluation can help identify concerns that may otherwise be overlooked. Women should be viewed not only as consumers of femtech, but also as active participants in shaping the technologies intended to serve them.

A Canadian Legal Perspective

AI-driven femtech is rapidly outpacing regulatory oversight and the frameworks guiding responsible innovation. Issues of consent, data use, and purpose limitation become more consequential given AI’s ability to analyze health information and generate individualized insights.

A forthcoming University of Ottawa research project will examine the privacy risks associated with femtech mobile applications, including how sensitive reproductive health information is collected and handled. Focusing on young women and minors, the project will assess whether existing Canadian privacy protections are adequate and consider reforms in response to these emerging risks.

Canada’s AI for All strategy frames the country’s approach to AI around three interconnected goals: fostering trust by protecting Canadians from AI-related risks and harms, creating opportunities for Canadians to participate in and benefit from AI, and safeguarding Canadian sovereignty through domestic data, talent, and infrastructure. For femtech, this strategy underscores the importance of pairing innovation with protections for sensitive health information and meaningful participation in how AI is developed and governed.

AI-driven femtech offers an opportunity to address longstanding gaps in women’s healthcare, but realizing that opportunity depends on responsible innovation and effective oversight. Biased data, narrow design assumptions, and the use of sensitive health information can reproduce existing inequalities and create new risks. Fulfilling femtech’s potential requires innovation alongside meaningful safeguards for transparency, accountability, safety, and fairness. In Canada, this means ensuring that emerging AI governance keeps pace with technological change while centering women’s experiences and needs in the case of femtech. Ultimately, AI should not simply make women’s healthcare more personalized and accessible—it should help make it more equitable, trustworthy, and responsive.