Artificial intelligence (AI) is reshaping healthcare, with the potential to improve diagnostics, drug development, and patient engagement. But without deliberate safeguards, AI risks replicating and accelerating the same gender biases that have historically excluded women from equitable care. From male-centric training data to the underrepresentation of women in clinical trials and algorithm design, bias is already shaping how AI tools diagnose disease, recommend treatments, and interact with patients. Without intervention, these systems could exacerbate existing disparities and undermine trust in digital health. To build a future where AI is a force for equity rather than exclusion, healthcare stakeholders must prioritize inclusive data practices, transparent development, and continuous bias monitoring.
The Promise and Peril of AI in Healthcare
Artificial intelligence (AI) is rapidly reshaping healthcare, unlocking new possibilities in diagnosis, drug development, public health monitoring, and patient engagement. The promise is enormous: faster identification of disease, more personalized treatment plans, and systems that can learn and improve over time. But the power of AI cuts both ways. Algorithms are only as good as the data they learn from — and only as objective as the people who build them.
Healthcare is already a field deeply shaped by gender bias. For decades, clinical research prioritized male bodies, often excluding women entirely from studies or failing to analyze results by sex. The result is a medical landscape in which women are more likely to be misdiagnosed, experience longer diagnostic delays, and receive less effective treatments for conditions that present differently in women, such as cardiovascular disease and autoimmune disorders
Without deliberate action to address these existing gaps, AI will not correct them. It will encode them into its models and automate their reproduction at scale. Rather than a revolution in equity, AI risks becoming yet another seemingly neutral technology that entrenches long-standing disparities faster, and more invisibly, than ever before.
Healthcare’s future will not be shaped by technology alone, but by the values, vigilance, and intentionality brought to its design and deployment. The question is not whether AI will change healthcare but whether it will do so in ways that advance or undermine equity.
The Root Causes of AI Gender Bias
AI systems are often described as objective, neutral, and evidence-driven. In reality, they are mirrors of the world in which they are created. Far from operating in a vacuum, AI tools reflect the social, scientific, and cultural biases embedded in their training data and design processes. In healthcare, this is especially dangerous because so much of the existing medical system has been shaped around male bodies, male experiences, and male-centric definitions of disease.
One of the most foundational causes of gender bias in healthcare AI is that training data are inherently historical data. Machine learning models depend on enormous volumes of past information to identify patterns and make predictions, but when it comes to healthcare, much of that information was generated in an era of exclusion. For decades, women were explicitly barred from clinical trials, particularly after the thalidomide tragedy of the 1960s led to protective regulations that, in practice, sidelined women from research entirely. Even after those rules were relaxed, women’s participation in trials remained limited, especially in early-phase drug development or studies involving complex medical devices.1,2
This underrepresentation means that AI systems trained on existing medical data may not have sufficient information about how diseases present in women, how treatments affect female physiology, or what symptoms signal risk for women-specific conditions. For example, heart disease — the leading cause of death for women — often presents with symptoms that differ from the so-called “classic” male pattern, such as chest pain. Despite these differences having been well established, many diagnostic tools have been trained on data sets where these differences are not adequately captured, increasing the risk of misdiagnosis or delayed treatment for women.1
Another critical source of bias is the composition of the teams developing AI systems. The field of AI is marked by significant gender disparities. Women are vastly underrepresented in technical roles, leadership positions, and AI research itself. This lack of diversity limits the perspectives that inform how AI models are built and evaluated. It shapes the questions developers think to ask, the types of data they prioritize, and the edge cases they prepare for — all of which can contribute to blind spots in how tools perform across different populations.3,4 In particular, studies have shown that more diverse development teams are better able to anticipate unintended consequences and to design systems that perform equitably across demographic groups.3 The absence of these perspectives increases the risk of overlooking gendered impacts.
The rapid rise of generative AI — systems that create text, images, or video based on their training data — introduces another layer of bias risk. These models are often trained on massive troves of online content, much of which is steeped in cultural stereotypes about gender roles. As a result, generative AI frequently replicates and amplifies these stereotypes, whether through biased language, inappropriate imagery, or flawed assumptions about user behavior. In healthcare settings, this can shape patient engagement tools in subtle but harmful ways — from virtual assistants that default to male-centric language to educational materials that fail to reflect the realities of women’s health.5,6
Perhaps the most insidious form of bias is what is missing entirely from healthcare data. Many medical records and research databases fail to capture sex-disaggregated data — information broken down by biological sex or gender identity. Without this information, AI systems cannot learn important patterns that differentiate how diseases progress or how treatments affect different patient populations. This problem is especially acute in areas like reproductive health, hormonal conditions, autoimmune diseases, and pain management, where women’s experiences often diverge significantly from men’s. The absence of these data makes it impossible for AI tools to offer truly personalized or equitable care.7,8
Moreover, even when data are collected, it is often framed through a male-default lens. For example, certain symptoms may be coded as “atypical” simply because they deviate from male-centric models of disease. This kind of framing influences both clinical decision-making and AI system outputs, reinforcing a cycle in which women’s health differences are seen as anomalies rather than essential considerations.8
In the end, gender bias in AI is not the result of malicious intent or programming errors. It is the logical outcome of systems built on incomplete, unbalanced, and often exclusionary foundations. Recognizing these root causes is essential because if bias is designed into the architecture of healthcare AI, it will take conscious, sustained effort to design it out.
Where Healthcare AI Is Being Used — And Where Bias Matters Most
AI is no longer a future technology in healthcare — it is already embedded across the continuum of medical research, diagnosis, treatment, and patient care. From drug discovery to digital health apps, AI tools are being integrated into every stage of the healthcare system. While this creates powerful opportunities for innovation, it also means that the risks of bias are broad, systemic, and compounding. Gender bias in AI is not confined to a single application or product; it has the potential to shape health outcomes across diverse and interconnected domains.
Diagnosis and Risk Prediction
One of the most heavily marketed uses of AI in healthcare is in clinical decision support, primarily tools that assist physicians in diagnosing diseases or predicting a patient’s risk of developing a condition. These tools rely on pattern recognition drawn from historical patient data, which, as discussed, are often skewed toward male presentations of disease. This is particularly dangerous in areas like cardiology, where symptoms of heart disease in women often differ significantly from the male norm.
For example, while chest pain is widely recognized as the primary symptom of a heart attack, women are more likely to experience fatigue, nausea, shortness of breath, or pain in the jaw or back. Yet AI systems trained predominantly on male data sets may not weight these symptoms appropriately, leading to missed or delayed diagnoses.9 Moreover, some risk prediction algorithms are explicitly calibrated using male-centric data, embedding a bias that disadvantages female patients before they ever see a clinician.1,10
This problem extends beyond cardiology. Conditions like endometriosis, polycystic ovary syndrome (PCOS), autoimmune diseases, and chronic pain disorders are already underdiagnosed in women — not because they are rare, but because diagnostic models are poorly attuned to how these conditions manifest across diverse bodies.
Drug Discovery and Development
AI is also transforming drug discovery and development, offering tools that can analyze massive data sets to identify potential drug targets, predict compound efficacy, and optimize clinical trial design. However, these systems depend on existing biomedical data, which means they are only as inclusive as the research history they inherit.
Clinical trial data has long been male-dominated, with women often excluded owing to concerns about hormonal variability, reproductive risk, or perceived complexity. Even when women are included, results are not always analyzed by sex, limiting the granularity of insights available to AI tools.11 This creates a pipeline in which new drugs may be developed, optimized, and approved based on models that do not fully capture sex-based differences in drug metabolism, side effects, or efficacy.
Further compounding the issue, the genetic and precision medicine databases often used to identify molecular targets or develop personalized therapies suffer from similar underrepresentation of women and marginalized populations. Bias in these databases can lead to treatments that are less effective for women or that fail to address sex-specific disease pathways.2,12 As the industry moves toward individualized medicine, failure to correct these biases risks entrenching inequities at the most advanced levels of care.
Epidemiology and Public Health
Beyond individual patient care, AI plays a growing role in population health management, epidemiology, and public health planning. Predictive models are increasingly used to forecast disease outbreaks, allocate resources, and design intervention strategies. But these models often fail to account for the gendered dimensions of health — including differences in exposure risk, caregiving responsibilities, occupational hazards, and access to healthcare services.
For example, during the COVID-19 pandemic, women globally experienced disproportionate burdens of unpaid care work, higher rates of job loss in certain sectors, and increased exposure risks in frontline healthcare roles. Yet many early models failed to account for these dynamics, leading to generalized predictions that obscured women’s specific vulnerabilities.13,14
Similarly, models used for maternal health tracking, reproductive health services, or vaccination campaigns may overlook critical factors like social determinants of health, cultural barriers, or disparities in healthcare access. This limits the effectiveness of public health strategies and undermines efforts to close health equity gaps.8
Digital Health and Wearables
Finally, AI-powered digital health tools — from wearable devices to virtual assistants — represent one of the fastest-growing frontiers of healthcare innovation. These tools promise greater patient empowerment, continuous monitoring, and personalized health insights, but, again, gender bias threatens to undermine their potential.
Many wearables, such as fitness trackers or heart rate monitors, are calibrated using male physiology, including average heart rates, body temperatures, and activity patterns. This can lead to inaccurate readings or misinterpretation of health data for female users, whose physiological patterns may differ significantly due to hormonal fluctuations, pregnancy, or other factors.10,15
Patient-facing AI tools like chatbots or symptom checkers may also exhibit biased language patterns, assumptions about user behavior, or oversights in addressing women’s health concerns. Studies have shown that virtual assistants often respond differently to male and female users or fail to recognize terms and symptoms more commonly reported by women.16 In a healthcare landscape increasingly reliant on digital engagement, these biases can create barriers to care and further erode trust in medical systems that already underserve women.
The Consequences If Bias Persists
The risks of unchecked gender bias in healthcare AI are not abstract or hypothetical — they are a health equity crisis in the making. Without deliberate efforts to identify and correct bias in AI systems, the healthcare industry faces a future in which existing disparities not only persist but become harder to detect and more difficult to overcome.
Women already experience worse health outcomes across a range of conditions, due in large part to long-standing diagnostic and treatment gaps. On average, women wait longer than men for a correct diagnosis — especially for conditions that manifest differently across sexes or that have historically been dismissed as psychosomatic when reported by female patients.1 Autoimmune diseases, chronic pain conditions, and reproductive health disorders are all characterized by lengthy diagnostic delays, with patients often cycling through multiple providers before receiving appropriate care.
If AI systems trained on male-dominated data sets are allowed to guide clinical decision-making without adjustment, these delays are likely to worsen. Diagnostic tools that fail to recognize sex-specific symptoms will continue to miss or misinterpret the presentation of disease in women. Conditions like heart disease, stroke, and neurological disorders already show clear patterns of sex-based differences in symptoms, risk factors, and progression — yet these differences remain underrepresented in many existing clinical data sets.9,15
Misdiagnosis or undertreatment of women is not just a matter of poor care — it can be fatal. Cardiovascular disease is the leading cause of death for women globally, yet both patients and clinicians often underestimate risk due to a lack of awareness and a reliance on male-centric diagnostic models. Autoimmune diseases disproportionately affect women, yet treatments are frequently tested and optimized based on male physiology.9,15 The stakes of gender bias in healthcare AI can truly be life and death.
Beyond clinical outcomes, the persistence of bias in digital health threatens to erode trust in healthcare systems, particularly among populations already underserved or marginalized. As AI tools become more visible in patient interactions, users will quickly recognize when these systems fail to reflect their experiences or respond to their needs. Chatbots that misunderstand symptoms, wearable devices that deliver inaccurate readings, or risk prediction tools that offer irrelevant advice will not only frustrate users but drive them away from digital health platforms entirely.17
This loss of trust is especially damaging because healthcare systems are increasingly relying on digital engagement to improve access, reduce costs, and empower patients to take a more active role in their care. If women and other marginalized groups feel excluded or misrepresented by AI tools, the result will be a widening gap in digital health adoption and a missed opportunity to close existing care disparities.
The economic and social costs of these failures are profound. Poor health outcomes among women have cascading effects on families, communities, and economies. In many parts of the world, women are primary caregivers, essential workers, and key drivers of social stability. When women suffer from preventable or poorly managed health conditions, the burden extends far beyond the individual patient. The World Health Organization and the United Nations have both emphasized that advancing gender equity in healthcare is essential not only for human rights but for sustainable economic development and public health resilience.18
Unchecked bias in healthcare AI risks compounding these challenges, locking in patterns of inequality at the very moment when new technologies could be leveraged to overcome them. Without intervention, AI will not be a tool for progress — it will be an accelerant for existing disparities, operating at a speed and scale far beyond traditional healthcare systems’ ability to correct.
Oversight, Regulation, and Data Reform Are Urgently Needed
The rapid adoption of AI in healthcare has far outpaced the development of the ethical, regulatory, and technical safeguards needed to prevent harm. While awareness of AI’s potential for bias is growing, current oversight mechanisms remain fragmented, voluntary, or advisory in nature. To ensure that healthcare AI serves all patients equitably and does not entrench existing disparities, a coordinated global effort is needed to establish stronger frameworks, enforce standards, and rebuild the foundational data that powers these systems.
Several international organizations have already recognized the risks of gender bias in AI and issued guidance aimed at promoting more ethical development and deployment practices. The World Health Organization, the United Nations, and the European Union have all released reports and recommendations urging developers to consider equity, inclusivity, and human rights at every stage of AI creation. These frameworks emphasize transparency, accountability, and the need to involve diverse voices — including women and marginalized populations — in shaping AI systems that will govern their health and wellbeing.6,14
One of the most critical recommendations from these bodies is the mandate for sex-disaggregated data collection. Without data that explicitly captures sex and gender differences, AI systems cannot be expected to recognize or address these factors in their outputs. Efforts are underway to update clinical trial reporting standards, electronic health record systems, and public health databases to ensure that sex and gender data are collected consistently and used appropriately in research and algorithm development.7,8 However, progress remains uneven across regions and healthcare systems, and many AI developers still rely on data sets that predate these standards.
In addition to better data collection, there is a growing call for algorithmic audits — independent, external reviews of healthcare AI tools to assess them for bias, transparency, and fairness. These audits would function similarly to clinical validation studies or regulatory inspections, providing assurance that AI models have been tested against diverse populations and evaluated for unintended consequences. While some companies have begun to implement internal fairness checks, experts argue that true accountability requires external oversight, with clearly defined benchmarks for performance across sex, gender, race, and other demographic factors.3,4
Unfortunately, regulatory frameworks for AI in healthcare have not kept pace with its widespread adoption. While medical devices and pharmaceuticals are subject to rigorous testing and approval processes, AI-driven software tools often fall into regulatory grey areas, particularly when marketed as decision-support tools rather than standalone diagnostic systems. The result is a patchwork of oversight that varies by country, product type, and intended use that leaves significant gaps where biased algorithms can operate without scrutiny.9
Ultimately, addressing gender bias in healthcare AI will require a fundamental rebuilding of the data pipeline that feeds these systems. This means designing more inclusive clinical trials that intentionally recruit diverse populations, including women of all ages, races, and health statuses. It also means capturing patient-reported outcomes and community-based data that reflect real-world health experiences — not just those filtered through traditional clinical settings.2,11
Community engagement is essential to this effort. Patients must be seen not only as data sources but as partners in shaping the future of healthcare AI. Initiatives that involve patients in study design, data governance, and evaluation processes will create systems that are more attuned to the lived realities of diverse populations and more resilient against bias.
Proof in Practice — Case Studies Where Bias Was Caught and Fixed
Heart Disease Risk Scoring Tools. Early AI-based cardiovascular risk models significantly underestimated risk in women. These tools were trained on datasets dominated by male subjects and failed to recognize how symptoms such as nausea, fatigue, or jaw pain — more common in women — signal cardiac distress. After analysis revealed gendered disparities in prediction accuracy, researchers revised the models to incorporate sex-specific symptom profiles and risk factors. The updated tools now demonstrate improved performance in assessing risk among women, reducing diagnostic delays and improving preventive care.
Speech Recognition in Healthcare Apps. Digital assistants and speech-enabled health applications originally showed lower accuracy when processing female voices, particularly those with higher-pitched tones or regional dialects. This created friction in user experience and increased the risk of misinterpretation in patient-provider communication. Developers responded by diversifying training datasets to include a broader range of female speech patterns and by adjusting acoustic models for pitch and cadence. These changes significantly improved recognition rates for female users and laid the groundwork for more inclusive patient-facing AI.
Drug Dosage AI Models. AI tools used in precision medicine for dosing recommendations were found to produce less accurate results for women due to their reliance on pharmacokinetic data primarily derived from male participants. This bias risked over- or under-dosing female patients, with potential safety and efficacy consequences. In response, developers integrated sex-disaggregated pharmacokinetic and pharmacodynamic data into model training, leading to recalibrated algorithms that better reflect differences in metabolism, hormonal fluctuations, and body composition. These more inclusive models are now helping to improve dosage accuracy and therapeutic outcomes for women.
Liver Disease Screening Algorithms. A study by University College London revealed that AI models designed to predict liver disease from blood tests were twice as likely to miss the disease in women compared to men. This discrepancy was attributed to the models being trained predominantly on male data. Upon identifying this bias, researchers advocated for the inclusion of sex-specific data in training datasets to enhance diagnostic accuracy for women.
Bacterial Vaginosis Diagnostic Tools. University of Florida researchers found that machine learning algorithms used to diagnose bacterial vaginosis, a common infection affecting women, exhibited diagnostic bias among different ethnic groups. The study highlighted the need for diverse and representative training data to ensure equitable diagnostic performance across all populations.
Pediatric Mental Health Screening. An AI model developed for detecting anxiety in pediatric patients was found to underdiagnose female adolescents due to linguistic differences in patient notes. Researchers implemented a data-centric de-biasing approach, which reduced diagnostic bias by up to 27%, demonstrating the effectiveness of targeted interventions in enhancing equity across demographic groups.
The Road Forward — AI as a Tool for Gender Equity
While the risks of gender bias in healthcare AI are serious and urgent, they do not represent an inevitable future. The same technology that threatens to replicate historical inequities can, if built with care and intention, help correct them. AI holds enormous potential not only to accelerate medical innovation but also to advance health equity — provided that systems are designed with inclusivity at their core.
Building equitable AI in healthcare begins with centering women and marginalized populations in every phase of development. This means ensuring that diverse perspectives are represented not only in training data sets but also on development teams, in leadership roles, and within governance structures. Tools that shape patient health should not be created in environments that exclude the very people they are meant to serve. From the earliest stages of product design, developers must ask whose experiences are reflected in their data, whose needs are prioritized in their models, and whose voices are present in decision-making processes.14
Transparency and inclusivity are equally essential. AI systems must be developed through participatory processes that involve patients, clinicians, ethicists, and community advocates, not just data scientists or engineers. This collaborative approach can help identify blind spots, challenge assumptions, and create tools that are more responsive to the complex realities of healthcare delivery. Open communication about how AI systems work, what data they rely on, and how they are validated will foster trust and accountability — key ingredients in any equitable healthcare system.16
Ensuring equity is not a one-time task but an ongoing process. AI models must be subject to regular monitoring for bias and updated as new data becomes available. Healthcare is dynamic — new treatments emerge, population demographics shift, and patterns of disease evolve. Static models trained on outdated or unrepresentative data will fail to keep pace with these changes. Organizations must commit to continuous improvement, expanding their data sets, refining their algorithms, and auditing their systems to ensure fair performance across diverse patient populations.10
If built well, AI can do more than avoid harm — it can actively help address the structural biases that have shaped medicine for generations. AI has the capacity to surface patterns in large data sets that might otherwise remain invisible, revealing gaps in care, disparities in outcomes, and unmet patient needs. With careful design, these insights can guide targeted interventions, inform policy, and create more personalized approaches to treatment that reflect the diversity of human health.8
Ultimately, the future of healthcare AI must be not only intelligent but equitable. Intelligence without equity risks reinforcing power imbalances and deepening health disparities. Equity without technological advancement risks leaving systemic problems unaddressed. The goal is not to reject AI but to reshape it — to demand systems that reflect the full complexity of human experience and that serve all patients with fairness, dignity, and respect.13
This is the challenge and the opportunity facing the healthcare industry today. The tools of the future are already being built. The question is whether they will replicate the inequities of the past or help create a new model of care, one in which technology is a force not of exclusion but of justice.
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