When a Generation Brings a Chatbot Into the Lab

By Mae Losito, MLT(ASCP), Microbiology
21 Years in Clinical Laboratory Science

For more than two decades, I’ve worked in clinical laboratory science — most of that time in microbiology.

I don’t experience public health through headlines. I experience it through specimens, test results, and timelines.

I’ve signed off on positive chlamydia screens from teenagers who were convinced it was “just irritation.” I’ve seen gonorrhea results in patients who delayed testing because something online told them it probably wasn’t serious. I’ve watched syphilis reappear in communities that thought it was a relic of the past.

So when I read that 20% of Gen Z adults have asked an AI chatbot about STIs — and that many were seeking an actual diagnosis — I wasn’t shocked.

What gave me pause was this: when those chatbot responses were compared to confirmed lab results, they were wrong nearly one-third of the time.

In microbiology, a 31% miss rate isn’t a harmless error.

It’s a system failure.

The Bench Doesn’t Guess

Sexually transmitted infections are clinically deceptive. Many produce no symptoms at all. Others look like common, less serious conditions — yeast infections, urinary tract infections, bacterial vaginosis, dermatitis, even normal physiologic variation.

A chatbot can analyze patterns. It can generate probabilities.

But in the laboratory, we don’t operate on probability alone.

We detect.

We run nucleic acid amplification tests (NAATs) to identify the genetic material of specific organisms. We confirm findings. We examine microscopy when indicated. We culture when necessary. Increasingly, we track antimicrobial resistance trends because treatment is no longer as simple as it once was.

Diagnosis requires evidence — an organism, its DNA, or a measurable response.

An algorithm cannot evaluate a specimen. It cannot measure bacterial load. It cannot detect co-infection. It cannot determine resistance patterns that are quietly evolving across regions.

And those details matter.

What I’ve Observed as Gen Z Came of Age

As Gen Z moved into late adolescence and early adulthood, I did notice a shift — but not the one critics like to assume.

It wasn’t recklessness.

It was hesitation.

More delayed testing.
More anxiety-driven testing.
More self-diagnosis before clinical evaluation.
More uncertainty about what symptoms actually mean.

There’s a visible gap in baseline sexual health knowledge that didn’t feel as pronounced 15 or 20 years ago.

National surveillance data consistently show that adolescents and young adults account for nearly half of reported chlamydia, gonorrhea, and syphilis cases in the United States. That statistic is often framed as a moral failing.

It isn’t.

It’s structural.

Over the years, comprehensive sex education has become inconsistent across states and districts. Curriculum battles have replaced clarity. At the same time, teens are living in a digital ecosystem where nearly half report being online “almost constantly.”

When reliable instruction contracts and screen time expands, information doesn’t disappear.

It relocates.

Right now, it’s relocating to AI.

The Risk of “It’s Probably Fine”

In microbiology, timing is everything.

Untreated chlamydia can progress to pelvic inflammatory disease.
Untreated gonorrhea can disseminate beyond the initial site of infection.
Syphilis can advance quietly through stages before symptoms become unmistakable.

Delay increases transmission risk. Delay increases complications.

What concerns me most about chatbot-based reassurance isn’t the technology itself — it’s the behavioral ripple effect.

If someone receives false reassurance, they wait.
If someone receives an inaccurate alarm, they panic.

In both scenarios, decisions are made without laboratory confirmation.

A chatbot is not CLIA-certified. It is not regulated as a diagnostic instrument. It does not carry clinical liability.

But patients carry the consequences.

This Isn’t About “Kids These Days”

Gen Z is not apathetic. In my experience, they’re thoughtful and cautious — sometimes to a fault. They care about privacy. They worry about cost. They worry about stigma. They don’t always feel safe asking parents, and they don’t always feel certain about accessing care.

When a 19-year-old tells me they “asked AI first,” I don’t hear irresponsibility.

I hear that the human system felt harder to approach than a screen.

That should concern us more than the technology itself.

Where Policy Inevitably Intersects

As a medical laboratory scientist, my role is detection, not legislation.

But after 21 years in this field, pattern recognition becomes second nature.

When medically accurate sexual health education declines, laboratory-confirmed infections do not vanish.
When confidential testing becomes harder to access, delayed diagnoses increase.
When curriculum is reshaped by ideology rather than evidence, the downstream impact eventually reaches the bench.

That observation isn’t partisan.

It’s empirical.

A Generation Deserves More Than a Guess

Artificial intelligence can be useful for general education. It can explain terminology. It can outline possibilities.

It cannot diagnose you.

If you have symptoms — or even if you believe you were exposed without symptoms — testing remains the only definitive path forward.

Not reassurance.
Not speculation.
Not probability.

Evidence.

For 21 years, I’ve trusted the bench more than assumptions. The bench does not shame. It does not moralize. It does not speculate.

It detects.

If we want better outcomes for Gen Z — and for every generation after — we need to restore three foundational pieces:

• Medically accurate, evidence-based education

• Accessible, confidential testing

• Clinical environments that feel safe and stigma-free

Because when a generation turns to an algorithm for diagnosis, that isn’t primarily a technology story.

It’s a trust story.

And trust, once lost, is much harder to culture than bacteria.

— Mae

About the Author
Mae Losito is a Medical Laboratory Scientist specializing in microbiology with 21 years of clinical laboratory experience. Her work focuses on diagnostic integrity, laboratory medicine, and public health education.