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It still shocks me that, for most of modern medicine, women weren’t even part of the data.

Until 1993, women were rarely included in clinical trials in the United States. The FDA recommended excluding women of “child-bearing potential” from early phases of drug studies, out of concern for pregnancy-related risk. As a result, entire generations of medications were tested almost exclusively on men.

That legacy hasn’t disappeared.

A large review of high-impact clinical trials found that only 24.6% of studies include men and women in roughly equal proportions. Even today, much of medical knowledge is still built on evidence that doesn’t fully represent half the population.

A classic example is heart disease. We’re taught that chest pain is the universal warning sign. But in women, that symptom is often less intense, atypical, or even absent. Women are more likely to experience shortness of breath, nausea, unusual fatigue, jaw or back pain, or what feels like indigestion.

Because these symptoms don’t match the “textbook” heart attack — a textbook historically written around male presentations — diagnosis and treatment are more likely to be delayed. Research shows that women having heart attacks are significantly more likely to present with three or more non-chest-pain symptoms compared to men.

I recently heard a great discussion about this in a podcast episode (linked here). It doesn’t just critique medicine — it challenges the assumptions that shaped it.

The Path Forward Isn’t Just Studying Women. It’s Personalizing Medicine.

Of course we need more research on women. That correction is overdue.

But the fastest path forward may not be slowly fixing historical datasets. It may be building healthcare systems that adapt to individual biology from the start.

What many call the “longevity trend” is really something deeper: care that is proactive, data-driven, and personalized.

This is where technology matters.

Personalized medicine won’t become standard because it sounds appealing. It will become standard because technology finally makes it scalable.

Where AI Matters Most: Closing the Care Gap

Patient-facing AI companions become interesting here, not as automation tools, but as a way to restore continuity to care.

This is where I’ll allow myself one brief shoutout. At Puppeteer AI, we focus on the patient-facing companion, not just front-desk automation, but the ongoing relationship that healthcare systems rarely have time to maintain.

A true AI care companion doesn’t wait for the patient to show up sick. It:

• Checks in regularly through voice or text
• Tracks symptoms and trends over weeks or months
• Asks follow-up questions a patient wouldn’t think to mention
• Flags changes that require clinical attention
• Schedules appointments only when medically meaningful
• Answers everyday questions that never reach a doctor

In practice, this creates a longitudinal clinical dataset that most physicians don’t have today - not because they don’t want it, but because the system doesn’t allow for it.

Right now, nearly all clinical decision-making happens based on what a patient remembers during a 12-minute encounter. An AI companion expands that clinical memory to every day of the year.

This isn’t about replacing doctors. It’s about giving them a more complete picture, early enough to intervene. When AI does the continuous listening, clinicians can do what humans do best: interpret, decide, and treat.

Why Doctors Should Pull AI In (Before Patients Push It In)

One line I heard recently stuck with me:

“Doctors need to pull AI into their practice, otherwise they’ll be bypassed by patients using it.”

That’s not a threat - it’s already happening. Patients are turning to AI companions, symptom checkers, and health apps because they can’t get answers fast enough inside the system.

If clinicians don’t offer responsible, medically guided AI tools, patients will adopt unguided ones on their own.

The urgency isn’t about technology trends. It’s about protecting clinical authority.

Either AI becomes an extension of clinical care - or it becomes healthcare outside clinical care. The choice isn’t whether AI will be used, only who leads it.

Building Healthcare Around Real People

For decades, medicine was built around a narrow definition of the “typical patient,” and women were often missing from that definition entirely.

Correcting that history isn’t just about adding more women to studies. It’s about rewriting how healthcare learns, listens, and responds.

Personalized medicine gives us the scientific direction.
AI companions make it operational.

They collect real-world symptoms, track changes over time, and make space for patients who don’t fit historical defaults. They help clinicians act sooner, with better information, and with care that reflects individual biology rather than outdated assumptions.

The future of women’s health — and really, of all health — isn’t reactive, episodic, or generic.

It’s continuous.
It’s individualized.
And it’s built on systems that can finally listen every day, not just at appointments.

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