Sahin Boydas
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Taking an AI Medical Device to Market

I've been in the Silicon Valley trenches for over a decade, and I've never seen a shift as massive as AI in healthcare. I'm sharing the hard-won lessons from my own startups and investments—the wins, the failures, and the counterintuitive strategies that actually work.

2024-04-286 min read563 words

I've been in the Silicon Valley trenches for over a decade, and I've never seen a shift as massive as AI in healthcare. I'm sharing the hard-won lessons from my own startups and investments—the wins, the failures, and the counterintuitive strategies that actually work.

The Reality Nobody Talks About

A lot of the advice on AI medical devices rests on assumptions that made sense five years ago. The world has moved on.

The first thing to understand is that simplicity beats complexity every time. I've seen this play out across dozens of companies. The pattern is unmistakable.

Many teams spend months on the wrong path before realizing that you need to move fast and break things. Once they make the switch, everything changes.

The Counterintuitive Truth

In AI medical devices, the best practitioners often do less, not more.

The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.

Real Talk: What Actually Matters

Here is what actually matters in AI medical devices.

First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI medical devices. Many fail because they moved too slowly.

Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.

Third, talk to your users. It sounds obvious, but many founders make decisions about AI medical devices in a vacuum. Get out of the building. Talk to real people.

This connects to broader themes around medical AI, clinical AI, AI mental health, biotech AI that I've been thinking about a lot lately.

What's Next

Best practice in AI medical devices is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.

My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.

If you're building in this space, the best time to get AI medical devices right is before you need to. Don't wait for a crisis to force your hand.

I'll keep sharing what I learn. This stuff matters too much to keep to myself.

FAQ

Frequently Asked Questions

Q:How can I apply this thinking to my own situation?

Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.

Q:What experience informs this perspective?

This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.

Q:Do all experts agree with this view?

No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.

Q:What's the most common pushback you get on this?

People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.

Written by

Sahin Boydas

Serial entrepreneur, #1 angel investor, author of Becoming Top 1%

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