The Invisible Bias: Minimizing Gender Bias During Data Analysis in Clinical Research and AI-Driven Medicine

Today, medicine is progressing at a rapid pace fueled by modern analytical approaches, artificial intelligence (AI), and precision medicine. However, one needs to face the uncomfortable reality that many of these modern approaches rely on years and years of data that is biased.

In medicine and healthcare, particularly, the issue is not only about who is conducting the research, but also about who is represented in the data generated. And historically, clinical and related basic studies have largely focused on the male sex, be it in neuroscience research—where male animals were used far more frequently than females, often to avoid complications associated with hormonal cycles—or cardiovascular disease research, which has heavily relied on male participants even though heart disease affects both sexes. In fact, major trials for cholesterol-lowering drugs included only about 28.5% women, leading to male-based evidence. More disconcertingly, the classic heart attack symptoms that have been presented in textbooks for a long time are largely based on male participants; today, it is known that women often present with different symptoms, and this bias has contributed to the high rates of under-diagnosis among women.

Historical bias in data and how it continues to be propagated

Clinical and biomedical datasets are usually a reflection of the assumptions made and constraints present at the time when the data were collected. And for decades, women were excluded or under-represented in research, particularly in early clinical drug trials and preclinical studies for varied reasons such as concerns about hormonal variations, or even simply because the male physiology was considered a universal baseline.

Between 1977 and 1993, regulatory guidance in the United States discouraged the inclusion of women of childbearing age in early-phase trials due to concerns about pregnancy risk and hormonal variability. One review of more than 43,000 clinical studies found that women were underrepresented relative to disease prevalence in 7 of 11 major disease areas, including HIV/AIDS and cardiovascular disease, the latter of which is a major cause of death among women!

This imbalance also extends beyond clinical trials. Preclinical and basic research in fields such as neuroscience and pharmacology have also largely relied on male animals and samples. Even diseases that disproportionately affect women—such as depression, Alzheimer’s disease, and many chronic pain conditions—were frequently investigated using male animal models. Interestingly, until as recently as 2016, scientists were using non-menstruating animal models to study menstruation because most laboratory animals such as rats do not menstruate!

And even though one might argue that these oversights were in the past, and that women’s physiology is taken more into consideration in clinical research today, these imbalances continue to matter because modern analytical systems depend heavily on existing datasets. Machine learning models are sensitive to training datasets; if certain populations are under-represented, the models cannot predict outcomes for those groups with the same certainty as it can for the dominant group.

This creates a compounding feedback loop, wherein biased historical datasets train AI systems, these AI systems generate biased predictions, and the biased outputs influence clinical decisions and future datasets. Over time, this cycle can propagate and even amplify disparities. This is especially true for the growing field of precision medicine, which relies on predictive models to make treatment recommendations. Even tiny biases in these models can have meaningful impact on diagnosis, treatment selection, and patient outcomes.

Thus, understanding and correcting gender bias in clinical datasets is essential, not just for ensuring gender equity but also for scientific accuracy and responsible healthcare.

Where does gender bias enter the research pipeline?

Gender bias in medical research can seep in at various stages, leading to biased data as the outcome. Identifying these entry points can help reduce the bias.

1. Data collection

Bias often begins during data collection. Women and subgroups, such as pregnant individuals or older women, have consistently been under-represented in early clinical trials and health studies. When the study population does not appropriately reflect the real-world disease population, the resulting evidence becomes skewed from the start.

2. Selection criteria

Even when women are included in studies, sex-specific variables such as hormonal status or reproductive health history are not always captured or incorporated into datasets. Excluding these variables can mask differences that influence disease risk, progression, or treatment response.

3. Data documentation

Diagnostic criteria and symptom definitions are often based on historical knowledge garnered from male-dominated research samples. As a result, symptom patterns more common in women may be under-recognized or inconsistently labeled in clinical datasets.

4. Model training

Machine learning systems trained on non-neutral datasets will internalize the bias. By its nature, algorithms will tend to learn patterns observed in the dominant group, thereby reducing predictive accuracy for under-represented populations.

5. Result interpretation

Finally, bias may appear in how findings are interpreted. The default physiological baseline has been derived from male participants, and clinical observations and results are often interpreted in comparison to this default baseline. However, deviations from the male physiology should not automatically be treated as atypical as they may be representative of the female physiology.

Addressing gender bias in clinical data

There are various ways in which one can begin to address gender bias in clinical data. While there is no single strategy that can completely eliminate bias, combining various approaches can substantially improve data reliability.

1. Audit datasets

A critical first step in data analysis should be to conduct bias audits before statistical analysis or model training. Particularly, researchers should evaluate whether the gender composition of the dataset reflects real-world disease prevalence and identify any potential representation gaps.

2. Use sex-disaggregated and stratified analysis

Even when datasets include both men and women, results are often reported only in aggregate form, which can miss any clinically meaningful differences in disease presentation, treatment response, or adverse drug reactions. Hence, outcomes of conducting subgroup analyses and validating predictive models across representative groups should also be reported.

3. Apply algorithmic fairness techniques

When machine learning models are used in clinical research, technical approaches such as rebalancing of datasets through oversampling underrepresented groups or reweighting training examples to increase representation influence can help reduce gender bias.

4. Diversify perspectives

Gender equity in research teams matters also for research outcomes. A diverse research team is more likely to ensure a fair study design, diverse participant recruitment, and accurate interpretation of outcomes appropriately considering all represented groups. Collaborating with external specialists—such as independent statisticians, clinical researchers, and data scientists—can also strengthen study design and bias detection. Platforms like Kolabtree, which connect organizations with experienced research experts, can be very useful in this regard.

5. Be mindful about secondary research

Meta-analyses and systematic reviews are powerful tools for clinical research as they analyze large, pooled datasets. However, these approaches often inherit biases from the studies they analyze. Therefore, it is extremely important to evaluate gender representation across contributing studies and accordingly conduct subgroup analyses to detect any hidden differences between sexes.

6. Report responsibly

Even with careful study design and analyses, some biases may persist. It is important to acknowledge these biases and report them transparently to inform future studies.

A gender-aware future

We stand at a point where it is equally important to ensure participation of women in research as well as ensure that the data guiding clinical research is free of gender bias. As AI and precision medicine become central to healthcare, addressing historical biases in datasets should become top priority. We also need to start moving beyond binaries and conduct sex- and gender-inclusive research to be able to fully represent human diversity.

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