The Hidden Code: Sex Bias in AI and Its Impact on Digital Healthcare

As artificial intelligence (AI) rapidly becomes embedded in digital healthcare systems, powering everything from diagnostic tools to personalised treatment recommendations, it promises a future of more efficient, accessible, and accurate medicine. Yet beneath this shiny facade of innovation lies a persistent and often overlooked problem: sex bias in AI. This bias is not simply a technical issue; it is a healthcare crisis in the making, one that risks distorting diagnoses, skewing treatment plans, and ultimately costing lives. 

Understanding Sex Bias in AI 

AI systems learn from data, and this data is often historical. Historically, medical research has focused predominantly on the male sex. Clinical trials, symptom descriptions, and treatment guidelines have for decades prioritised male patients, resulting in datasets that underrepresent or misrepresent female physiology and health experiences. When AI is trained on such biased data, it inherits and perpetuates these disparities. 

To illustrate, consider the following: 

• Heart attack symptoms in women often differ significantly from those in men, yet many diagnostic algorithms are primarily tuned to male presentations, increasing the risk of misdiagnosis. 
• Pain levels and autoimmune diseases, conditions which disproportionately affect women, are frequently downplayed or misunderstood by AI tools developed on male-centric data. 
• Mental health assessments can fail to recognise how disorders like depression or ADHD present differently between sexes, potentially delaying or misdirecting treatment. 

Real-World Consequences in Digital Healthcare 

Digital healthcare platforms increasingly rely on AI to automate triage, support clinical decisions, and personalise care. When sex bias infiltrates these systems, the effects are wide-ranging and serious. 

Women may face a higher likelihood of false negatives or delayed diagnoses, particularly where sex differences are well documented but underrepresented in training data. AI systems may suggest treatments that are less effective for women or exclude female-specific factors such as hormonal influences or pregnancy status. 

Moreover, marginalised groups, especially women of colour, can experience compounded bias, worsening existing disparities in access, quality, and outcomes. When digital tools fail to account for sex-specific needs, trust in digital healthcare can erode, particularly among women who already often feel dismissed or unheard in traditional medical settings. 

Addressing the Bias: Towards Sex-Inclusive AI 

Mitigating sex bias in AI-powered healthcare demands a strategic and collaborative approach: 

• Inclusive data collection is essential, ensuring datasets are balanced and disaggregated by biological sex, with algorithms tested against diverse populations. 
• Bias auditing and transparency must become routine, with healthcare institutions and developers committing to regular reviews and open methodologies. 
• Interdisciplinary collaboration brings together researchers, clinicians, and technologists to ensure AI reflects a complete understanding of human health. 
• Patient-centred design is critical, involving individuals from different sex groups in the creation and testing of digital tools to foster empathy, responsiveness, and equity. 

Shaping a Fairer Future for Digital Health

AI holds tremendous potential to transform healthcare, but only if it avoids replicating past injustices. Recognising and addressing sex bias in digital healthcare is not merely a technical challenge; it is a moral and strategic imperative. By prioritising precision and inclusivity in development, healthcare organisations can build systems that deliver equitable care for all. 

At HETT 2025, we’ll be diving deeper into the role of inclusive AI in digital health. Join us to explore how we can co-create technologies that reflect and serve everyone, not just the majority. 

Join us at our upcoming event, HETT Show on 7-8th October at ExCeL London to be part of the conversation. Register below.