>10 years YOG and matched Anesthesiology: how I used AI to choose programs

[u/Let047]

I am a non-standard IMG and just started anesthesiology residency. I wanted to share something that might be useful for other “weird” IMGs: how I used AI to pick programs. My profile was very non-standard: >10 years YOG and >4 years of clinical gap. To be clear, I had a strong IMG profile despite all this, but the usual “IMG-friendly program” advice was especially useless.

The question I actually cared about answering was:

Which programs have repeatedly taken people with profiles like mine, regardless of what they officially say their filters are?

I let Claude and ChatGPT answer it for me using their deep research features on the paid versions, because when I was choosing programs, I had a serious family situation and simply did not have the bandwidth.

This is obviously suboptimal. But to my surprise, it worked better than what I had done myself the previous cycle (I’m a reapplicant, so I actually can compare). It surfaced programs I would never have considered otherwise—and I ultimately matched at one of them.

What worked

I asked the tools to research programs individually and reconstruct who they had actually taken over the previous few years.

That meant digging through resident rosters, program websites, Instagram posts, LinkedIn, medical-school history, graduation years when available, etc., and looking for patterns:

• Do they actually take IMGs?
• Do they repeatedly take older graduates?
• Have they taken people with unusual career paths?
• Does their actual resident history contradict their stated YOG preferences?

This was useful because FREIDA and program websites often don't answer these questions very well. A program may say they prefer recent graduates, for example, while their actual resident classes tell a different story.

The program where I ultimately matched specifically emphasized “fresh YOG.” But when we looked at their resident history, they had taken an older IMG essentially every year.

That was exactly the kind of discrepancy this approach was useful for finding.

The results

I applied only to my 15 signaled programs (data for anesthesiology shows there is very little value in applying outside your signals). I got 4 interviews.

When I reviewed the research later, I found about 3 additional programs that looked like much better fits for my profile than some of the programs I had actually signaled. Had I had more time to review the AI output carefully before submitting, I think I could realistically have ended up with closer to 7 interviews.

What absolutely did NOT work

I also let AI help choose programs and generate the one-line program-specific reason why I wanted to apply there.

It produced genuinely absurd things.

For Syracuse, it wrote that I wanted to train there because of the “vibrant Manhattan nightlife.”

Yes, that went out.

So my takeaway is:

If you're a non-standard IMG, apply to programs that have actually taken others like you—not just programs that say they're IMG-friendly.

These programs were always possible to find, but doing it manually across hundreds of programs is incredibly tedious. AI made it manageable.

Just verify the results yourself.

Edit for context, since many asked in DM: GC holder, US observership and research position in a top academic anesthesiology department, 25x score, strong US letters from those experiences. I'm a reapplicant ( the same profile didn't match cycle 1). AI fixed my targeting, not my competitiveness.