AI’s Internal Gender Bias: The Risks of Further Integrating AI Into Medicine

Introduction

Artificial Intelligence is no longer a futuristic concept in healthcare - it is the silent engine behind modern diagnostics, drug discovery, and personalized treatment plans. We are told that AI is objective, data-driven, and immune to the "gut feelings" or implicit biases that plague human doctors, but this is not true. Algorithms are trained on historical data, and if that data is skewed, the AI doesn’t just reflect our past mistakes, it automates them. For women and marginalized groups, the integration of AI into medicine isn't just a technical upgrade; it’s a new frontier of risk.

The primary danger of medical AI lies in the gender data gap. For decades, the "default" medical subject was a 70kg male. From clinical trials to symptom checklists, women were often excluded due to "hormonal complexities." When an AI model is fed thirty years of medical records to "learn" how to identify a disease, it inherits a world where women were under-researched and under-diagnosed. The result? The AI develops a "male-as-default" internal logic.

A Case Study in Disparity: The UCL Liver Disease Research

While there are many examples of this playing out in a clinical setting, we only need to look at a landmark 2022 study by researchers at University College London (UCL), published in BMJ Health & Care Informatics.

The researchers analyzed several AI models designed to predict liver disease using standard blood tests. On the surface, the models appeared highly successful, boasting accuracy rates over 70%. However, when the researchers peeled back the layers and looked at the results by sex, a dangerous reality emerged.

Their Findings:

• The Performance Gap: The AI was twice as likely to miss liver disease in women as it was in men.

• The Numbers: The models failed to diagnose 44% of women who actually had the disease, compared to missing only 23% of men.

• The "Hidden" Bias: The AI relied heavily on biomarkers like albumin and enzyme levels. However, these markers often present differently in women. Because the AI was trained on a "mixed" dataset dominated by male physiological patterns, it treated the female presentations as "noise" or "normal," essentially leaving nearly half of the female patients in the dark.

"High accuracy overall may hide poor performance for some groups," noted Dr. Isabel Straw, the study’s lead author. "We need to ask: accurate for who?"

(Article: https://informatics.bmj.com/content/29/1/e100457)

The Snowball Affect Post Diagnosis/Scaling of Inequality

The dangers of internal bias reach far beyond just one organ; they create a huge ripple effect across the whole healthcare system. If we start using these algorithms without double-checking them, we risk automating some really dangerous double standards. For example, some AI tools used to manage pain medication actually underestimate what female patients need because they’re repeating old biases that claim women’s pain is just "emotional." This bias also causes AI to miss heart attacks, since the software is mostly trained on "classic" male symptoms like sharp arm pain, while ignoring the nausea or extreme exhaustion that women usually feel. Essentially, if an AI incorrectly labels a woman as "low risk" because its data is flawed, she might never get the life-saving specialist or treatment she actually needs.

The Path Forward

I'm not writing this blog with the thought that AI is bad and unhelpful as a whole, I do recognize it's capabilities and its potential to really help improve medicine and peoples quality of lives. For example, it can help all of the people who unfortunately aren't able to afford insurance and go see doctors in a clinic. Rather than having to figure it out on their own, they can consult an AI "doctor" online and get further instructions for their problem. It seems like an amazing advancement, however the key part that most people look over is that the AI might have biases and flaws that aren't getting checked - and this isn't something trivial, giving inaccurate medical advise can have serious effects on peoples lives.

The danger isn't the AI itself, but the untested trust we place in it. To make AI a safe tool for everyone, there are steps that need to be taken. I recently took an online course by Cambridge University that explained the basics of ethics in AI, and I learned that the most important factors when working with AI are fairness, accountability, transparency, safety, and privacy. These ideas mostly speak for themselves, but in summary, AI should treat people equally, be designed so humans can be held responsible for its actions, clearly show how and why it makes decisions, avoid causing harm, and protect personal information. Without these principles guiding its development and use, AI can easily reinforce bias, spread misinformation, or invade privacy - turning a powerful tool into a serious risk rather than a benefit.

The main way we can strive for these things is by making sure they are implemented during the training of AI, through ethical prompt engineering as well as the use of unbiased data sets. Prompt engineering is the process of carefully designing the instructions given to an AI so that it responds responsibly, avoids harmful assumptions, and follows ethical guidelines. When done correctly, it helps reduce bias, improve transparency, and ensure safer outputs. At the same time, using diverse and representative data sets is crucial, because AI systems learn directly from the data they are trained on. If that data is biased or incomplete, the AI will reflect and even amplify those biases. Together, ethical prompt engineering and unbiased data help ensure that AI supports fairness and accountability rather than undermining them.

To relate this to healthcare, ethical AI is especially critical because its decisions can directly affect patient outcomes. Using gender-disaggregated and ethnically diverse data ensures that AI systems are trained on populations they are meant to serve, reducing disparities in diagnosis and treatment. Algorithmic transparency is equally important, as doctors must be able to understand which factors an AI uses when making medical decisions in order to trust, question, and verify its recommendations. Finally, just as medical devices require regular calibration, AI systems need continuous bias audits to ensure their internal decision-making does not unintentionally widen existing gaps in patient care. When these safeguards are in place, AI has the potential to improve healthcare outcomes rather than reinforce existing inequalities.

AI has the potential to save millions of lives, but only if we ensure it recognizes all lives equally. Until then, the greatest danger in the room isn't the disease - it's the algorithm that can't see it.