Amy had just turned 25, but her body seemed to be turning against her: crushing fatigue, bone density loss, and months without getting her period. We saw multiple doctors. They suggested allergies, burnout, or that maybe she just needed more sleep. Then we got her MRI results back: My girlfriend had a brain tumor.

Amy has a prolactinoma, a tumor of the pituitary gland, the pea-size organ at the base of the brain that controls your hormones. In many cases, prolactinomas are benign, slow growing, and shrink with medication. The growth was caught late, gaining mass fast, and sitting in a rare position that threatens her vision. And Amy’s hormone levels were wildly elevated.

We were told that a standard course of treatment—including two surgeries—would get rid of the tumor entirely. It didn’t. The tumor kept coming back. I spent weeks as her full-time nurse, holding her hand through headaches that left her bedridden. She couldn’t bend over, couldn’t blow her nose for fear of a spinal fluid leak. One night, desperate for answers, I started talking to an AI chatbot. It was then that I had a wild thought: I’m going to cure her myself.

I started by describing Amy’s exact case: her tumor type, its position, her pathology results, and her response to different medications. Within my first week talking to chatbots, I found a paper that leading pituitary scientists told me they hadn’t seen, describing a compound that outperformed the leading treatment for drug-resistant prolactinoma cells. One researcher nearly jumped when I told him—he said he’d never seen any published literature make that connection, and that he was going to pursue it, stat.

I’m not the only person turning to AI in the fight against stubborn medical conditions. One-third of Americans are now turning to chatbots for health advice or information, according to a poll released in March. After I published Amy’s story on X, over a million people saw it. Among them was a software engineer whose young daughter has leukemia. He told me he learned more talking to a chatbot than in months spent in and out of doctors’ offices. AI is even helping cure diseases in pets: In Australia, a man recently used a chatbot to help scientists develop a customized cancer vaccine for his dog.

To be fair, you probably still need some level of expertise to navigate this brave new world of medical knowledge. I have a background in biophysics research and I’ve worked with AI and biotech start-ups for several years. I’d followed stories of patients like Sid Sijbrandij, the GitLab co-founder who built a full genomic research stack to fight his own cancer. This territory is more familiar to me than most.

Chatbots aren’t perfect; you still have to interpret the information they spit out, and they can’t replace doctors. But for the first time, a motivated person can show up to a medical appointment with knowledge that was inaccessible five years ago. AI can comb through research papers, drug trials, and obscure morsels of medical knowledge, and make them available to anyone. In the very near future, cutting-edge advances in medicine may arrive through the work of motivated patients and caregivers who know how to use these tools.

After Amy’s diagnosis, I started by reading everything I could find. Tens of thousands of papers have been published on brain tumors, and no single doctor has time to synthesize all of the details relevant to a single patient’s case, so I used AI to help.

Here’s what I’ve found so far. Amy is taking a drug called cabergoline, which works by binding to a receptor on her tumor cells to halt cell reproduction. It’s the standard treatment, and it’s working. The tumor’s growth has halted. Still, about 10 to 15 percent of patients develop resistance to the drug, and when it stops working, their options begin to thin out.

I wanted to know: What happens if it stops working? And can we do anything now to make sure that doesn’t happen? These questions ultimately guided me to my first major victory—finding the paper that leading pituitary researchers hadn’t seen.

I also wanted to know: How did my healthy 25-year-old girlfriend end up with a brain tumor? When I asked one of the world’s leading prolactinoma experts what causes these tumors, he shrugged: “It’s random, but there’s possibly some genetic weighting.”

So I looked into gene therapy. I cross-referenced Amy’s pathology report and family history against the published literature on hereditary pituitary tumors and identified three genes worth testing. If any of these genes carried a mutation, it would mean Amy’s tumor had a hereditary cause, with implications for how aggressively to treat it and whether our future children could be at risk.

When I brought this to her neuroendocrinologist, I was nervous. I felt underqualified to raise something her own team hadn’t mentioned. To my delight, they were receptive, appreciative of the new idea, and willing to order the panel. Amy tested negative, which was a huge relief. It meant her tumor was most likely sporadic—that is, random—not inherited. This test only happened because AI helped me know what to ask for and why.

It’s important to note that these tools aren’t magic. They hallucinate. One claim turned out to be based on primate brain studies, not human tumor data. Another time, a model cited a paper that didn’t exist. But the best technique I’ve found is also the simplest: I run the same question through two different AI models and compare their answers. When two AI models converge on a finding, I trust it more. When they diverge, that’s where the interesting questions hide. I bring those disagreements to human scientists, and to Amy’s doctors.

It’s been three months since I started working on this full time, and the process has completely changed how I think about medicine. I’ve had over 100 meetings with neurosurgeons, pharmacologists, DNA analysis companies, and AI tool builders. The biggest barrier to research breakthroughs, I’ve realized, is that different areas of medicine are very siloed.

The scientists who study drug resistance in brain tumors publish in one set of journals. The chemists working on new ways to target the same receptor publish in another. They think in different frameworks and contexts. An AI that has ingested both literatures can see a bridge between them that no individual researcher has reason to look for. These tools are helping me understand Amy’s tumor biology now, while her medication is working, so we have a plan if it ever stops.

So far, Amy has had two brain surgeries. The first removed most of the tumor. After weeks of recovery, we were so hopeful we celebrated by trekking to the base of Mt. Everest. Then the tumor started regrowing. The second surgery was declared a complete success, until the labs came back worse than before.

The good news is she’s not dying. The tumor hasn’t spread. But a third surgery carries a 5 to 15 percent risk of permanent pituitary damage. And there’s always the possibility that the cabergoline stops working.

That’s why every morning before she wakes up, I’m at my laptop, reading papers and running prompts. Maybe I’ll find a link between two studies that convinces a researcher to test a compound on prolactinoma cells that’s only been tried on other tumor types. Maybe I’ll identify a mutation in Amy’s tissue that matches an existing drug nobody thought to try for her condition.

At minimum, I’ll walk into every appointment with better questions, and her care team will work with more context than they’d have without me. Instead of feeling dependent on a system that failed to promptly diagnose a large brain tumor in a healthy 25-year-old, I’m taking steps that may meaningfully help her and others.

Amy’s energy is coming back. Last month, she went skiing and traveled to Argentina. Her latest tests showed that her hormone levels are back to normal. But the fight isn’t over when the numbers look good. It’s over when we understand the biology of Amy’s tumor well enough that it can never surprise us again. We’re not there yet. But we’re closer than we were.

When people get a diagnosis like Amy’s, they learn to accept the things they can’t control. They show up to appointments, sit in waiting rooms, and put their faith in the people who went to school for this. For most of human history, that was the only logical response. Not anymore.