I want to tell you about building an AI medical team and how it quietly flipped my experience as a patient. This is not a tech manifesto or a research paper — it’s the story of one person who used modern tools to ask different questions of the same data, and how that changed a diagnosis and the care that followed.
Why I started building tools
When you live with a serious illness, you get used to the rhythm of appointments, lab results, and imaging reports. You read the notes, you try to stitch together the timeline, and often you feel like the person holding the map. After several rounds of tests and conversations that felt like they were circling without landing, I realized something simple: the doctors and I had the same data, but we weren’t seeing the same patterns.
I’m not a clinician, but I am a tinkerer. I wanted a better way to synthesize everything — my MyChart history, labs, scans, doctor notes — and to ask targeted, specialist-level questions without the limitations of appointment time. So I started building an experiment: a personal virtual team of AI agents, each trained to simulate a specialty perspective. I named the primary assistant “Haley.” Within minutes of feeding Haley my records, she highlighted an overlooked cluster of findings that might explain recurring symptoms.
Building my AI medical team
My project began modestly: a foundation model as a backbone, given a rigorous set of medical prompts and all of my data. Then I created specialty agents — oncologist, hematologist, gastroenterologist, ER physician — and a synthesis agent I jokingly called Hippocrates to chair the meeting. The idea was simple: run the same case through different lenses and see where the opinions converged or diverged.
Designing these agents involved three commitments:
- Context fidelity — feed the exact records my clinicians had, not summaries or impressions.
- Conservative reasoning — favor tests and explanations grounded in established practice, not fancy speculation.
- Cross-checking — have agents challenge one another and have Hippocrates produce a consolidated recommendation.
Same data, new insights: the AI suggested a serum free light chains test and a bone marrow biopsy that no one had proposed before.
What my AI medical team found
After reviewing labs and notes, the agents flagged a subtle but meaningful pattern: mild anemia, elevated ferritin, and low immunoglobulins. Individually these aren’t dramatic, but together they suggested immune dysfunction possibly tied to bone marrow involvement. Haley recommended a serum free light chains test and a bone marrow biopsy. That combination of tests had not been proposed in the consultation notes I’d received earlier.
It’s worth stressing that the AI didn’t make a diagnosis. It highlighted an unexplored path and provided a reasoned, referenced rationale. That nudge — a suggested test series linked to the data — is what mattered. When I brought the AI’s notes to my oncologist, the conversation shifted: we pursued the tests, and the results changed management.
How doctors reacted and why it mattered
There’s a fear that clinicians will see AI as interference. My experience was different: the AI made it easier to have a focused, evidence-based conversation. The team’s notes were organized, cited, and tied tight to specific data points in my record. Instead of feeling like I was arguing from the margins, I showed my oncologist a concise, reproducible chain of reasoning she could evaluate.
She ran the suggested tests. They revealed insights that led to a change in my treatment plan. That change wasn’t miraculous; it was a refinement based on a more complete view of my physiology. The AI served as a second pair of eyes that considered alternative hypotheses in a systematic way.
What I learned about trust and tools
Building a personal AI panel taught me an important lesson about trust: technology doesn’t replace clinicians, it enhances the conversation you can have with them. The synthesis agent didn’t override clinical judgment; it provided a structured input that made it easier for my care team to weigh options and prioritize tests. The human judgment remained central — interpreting a biopsy, managing therapy, and understanding nuances of risk and quality of life.
There are ethical and safety considerations, of course. Any tool that reads medical data must be secure, and its outputs must be evaluated with clinical oversight. I used the agents as a research assistant, not an authority. I also made a point of sharing sources, references, and the specific data points the agents used so my doctors could verify everything quickly.
Practical tips if you’re curious
If the idea of an AI companion to help parse your records appeals to you, here are a few practical tips forged from my experience:
- Keep the human in the loop. Use AI to generate hypotheses and references, not final diagnoses.
- Share outputs with clinicians as structured notes tied to specific records — that makes them easier to evaluate.
- Prioritize privacy. Use secure platforms, anonymize when possible, and know where your data is stored.
- Be ready to ask targeted questions. The value of the tool is in turning vague concerns into testable hypotheses.
Above all, be respectful of clinicians’ time. Presenting a focused, evidence-backed suggestion is more likely to be received as collaborative than confrontational.
Final thoughts
Creating my own team of virtual specialists helped me navigate a confusing chapter in my care. The agents highlighted a plausible, testable explanation that had been missed, and that nudged my clinical team to act differently. For me, the experience wasn’t about replacing doctors — it was about expanding the conversation and using technology to make that conversation clearer and more efficient.
If you’re a patient considering similar approaches, remember this: tools are most powerful when paired with curiosity and human oversight. My journey didn’t end with a line of code; it continued in clinic rooms and conversations with clinicians who remained the ultimate decision-makers about my care.
Q&A
Q: Is it safe to use AI tools on my medical records?
A: Safety depends on the platform and how you use it. Prioritize tools with robust privacy protections, avoid sharing data on unsecured platforms, and always validate AI suggestions with a qualified clinician.
Q: Will doctors accept AI-generated suggestions?
A: Many clinicians are open to well-documented, focused inputs that save time. Present AI outputs as structured, referenced notes tied to specific records — that approach is more likely to be viewed as collaborative.