Why AI Development Needs to Prioritize Women 50+

Let’s be blunt: if you’re a woman over 50, you’ve likely been overlooked more times than you can count – in advertising, in healthcare, and now, in the brave new world of artificial intelligence.

But this isn’t just another gripe about being ignored or even insulted. This is about health, wellbeing, and the disturbing fact that the most powerful tools shaping the future of medicine might not even know you exist.

Welcome to the strange new frontier where data rules and women 50+ barely register.

This was the original title image AI generated when using this prompt:

Woman 60-plus, mahogany coloured hair, very short pixie cut, dressed in casual golden-yellow sweatshirt. She is standing in front of a huge widescreen, in a high-tech AI environment.

It seems AI doesn’t really know what women aged 50 or 60 and above look like – I always get images of rather young, very slim women, but with hairstyles typical of older people.

The Invisible Demographic

Women over 50 are not a niche market – they’re a demographic powerhouse. And yet, in the world of medical research and tech development, we’re still treated like a rounding error.

The Historical Exclusion

“If you look at the history of medical research, women were routinely excluded from clinical trials until the early 1990s,” says Dr. Elena Rodriguez, an expert in women’s health. Why? Because hormones made things “too complicated.” So instead, researchers just studied men – and pretended women’s bodies worked the same way. (Spoiler alert: they don’t.)

The result? A knowledge gap so big, you could drive a tour bus full of menopausal women through it. And while the NIH began requiring the inclusion of women in 1993, the damage was done. Especially for older women, who are still vastly underrepresented.

A 2023 analysis in The Lancet showed that women over 50 made up just 12% of clinical trial participants – despite representing nearly 20% of the population in developed countries. That’s not a small oversight. That’s systemic invisibility.

By the way, this isn’t just a thing in medical research – Social Sciences have their quirks too. When I finished my first doctorate, my supervisor pointed out that most user interface studies rely on white male students. He was actually relieved we didn’t have to use rodents – because he was worried to get way too attached to those little critters.

Digital Ageism: A New Twist on an Old Problem

Just when we thought we were catching up, along came AI. And with it, a fresh version of the same old bias.

Enter digital ageism – a fancy term for what happens when tech is built by and for younger people. “It appears in assumptions about older adults’ capabilities, in designs that ignore vision or dexterity changes, and most importantly, in the data used to train AI systems,” explains Dr. Maya Patel, a technology ethicist.

The numbers are shocking: a 2024 study in “npj Digital Medicine” reviewed 78 healthcare AI systems and found that adults over 65 were underrepresented in 83% of the datasets. And women over 50? Represented in fewer than 5% of them.

Double Trouble: Gender Bias + Age Bias

“When we talk about bias in AI, we often discuss gender and age separately,” says AI researcher Dr. James Wilson. “But for women over 50, these biases compound – making them essentially invisible to AI.”

A 2023 study in JAMA Internal Medicine backs him up: AI diagnostic tools performed worse for postmenopausal women than for any other group. Why? Lack of representation in training data. “It’s a classic case of ‘you can’t be what you can’t see,’” adds Dr. Rodriguez.

Bias in, Bias out: How AI Can Worsen the Problem

You’ve heard the phrase “garbage in, garbage out”? That’s AI 101. If flawed data goes into the system, the AI just learns those flaws by heart.

“AI systems learn from historical data,” says Dr. Wilson. “If that data includes the underdiagnosis of heart disease in women or dismissals of menopausal symptoms, those mistakes get baked in.”

Real-Life Consequences

A Nature Medicine study found an AI skin cancer tool was 12% less accurate for women over 60.
Another system underestimated hospital readmission risk for women over 50 (Journal of the American Medical Informatics Association, 2023).
And a 2024 ProPublica report found AI used by insurers denied osteoporosis treatment to women over 60 – due to biased training data.

“These are real systems, affecting real women, right now,” says Dr. Franz (yes, this is me, in one older article).

AI Can Create a Vicious Cycle

“If an AI system misdiagnoses women over 50, it can lead to worse care,” warns Dr. Sarah Chen. That leads to poorer outcomes, which produces more biased data – and the cycle continues.

“It’s a digital version of the Matthew effect – the rich get richer, and the poor get poorer,” explains health equity researcher Dr. Sarah Chen. “In this case, groups that historically received better healthcare continue to do so, while those who were marginalized become further marginalized through algorithmic reinforcement.”

Why This Demographic Is So Unique (and So Ignored)

AI’s blind spot for women 50+ isn’t just unjust – it’s dangerous. Because our bodies are changing in ways that younger researchers (and algorithms) don’t fully grasp.

Menopause and Beyond

“Menopause isn’t just hot flashes,” says endocrinologist Dr. Rebecca Martinez. It affects bone density, metabolism, immune response, brain chemistry, and more. Cardiovascular risk goes up, but symptoms look different than in men. If AI systems don’t account for this? Misdiagnoses happen.

Multiple Conditions, Interacting in Complex Ways

Women over 50 are often juggling several health issues at once: osteoporosis, heart disease, autoimmune disorders. “These don’t exist in isolation,” says Dr. Franz. “AI needs to recognize how they interact – but it can’t if the data’s missing.”

We Present Differently

Women’s heart attacks often show up as back pain, fatigue, or shortness of breath – not the textbook chest-clutching you see on TV. If AI hasn’t been trained on these patterns, it’ll miss them.

We React to Meds Differently

As Dr. Lisa Murray points out, “Pharmacokinetics change with age and menopause.” That means AI needs to understand how medications behave differently in our bodies. Right now, it often doesn’t.

The Research Gap That Keeps Biting Us

Even with NIH policies requiring more inclusivity, the data gap persists. A 2023 Journal of Women’s Health study found that only 31% of trials analyze results by both sex and age. That’s a problem.

“AI developers work with the data that exists,” says Dr. Wilson. “If the underlying literature lacks older women, so will the AI systems.”

And it shows. One AI system missed 23% of high-risk osteoporosis cases in women over 65 (JAMA, 2022). Another failed to catch 35% of adverse drug reactions in postmenopausal women (2023).

“These aren’t small misses,” warns Dr. Franz. “They’re life-altering.”

The Hidden Cost: What This Means for Society

Ignoring women over 50 isn’t just bad ethics – it’s bad economics. “We’re talking about the primary users of healthcare services and often the decision-makers for entire families,” says health economist Dr. Michael Chen. When our care is poor, costs rise – for everyone.

And let’s not forget the human toll: avoidable suffering, lost independence, and diminished quality of life. “We’re not talking about five rough years,” says Dr. Franz. “We’re talking about decades that could be vital, creative, healthy – but aren’t, because tech didn’t think to include us.”

There’s Hope – But We Have to Act

This isn’t all doom and gloom. Solutions are on the table.

Smarter, More Inclusive Data

Efforts like the Women’s Health Initiative (WHI) – with data from over 161,000 postmenopausal women – are a goldmine for AI, if developers choose to use it. New data-sharing initiatives and better recruitment for clinical trials are starting to close the gap.

Participatory Design

Want to build something for women over 50? How about asking us? Participatory design puts older women at the centre of tech development – not as subjects, but as collaborators.

Conclusion: This Isn’t Just About AI – It’s About Agency

The fight for visibility in AI is about more than just better algorithms. It’s about reclaiming our power.

Women 50+ are not some statistical afterthoughts. We’re caregivers, leaders, changemakers – and yes, patients with complex, evolving health needs. We deserve tools that reflect our realities, not erase them.

It’s time we stop accepting invisibility as inevitable. Let’s demand technology that sees us, hears us, and understands us.

Because if the future is being written in code, we damn well better be part of the programming.

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