''' I’m going to say something that might make a few people in Silicon Valley angry. The "AI in Radiology" space is a bubble. And it's about to burst.
I’ve seen this movie before. I’ve been building and investing in tech companies for over a decade. I was lucky enough to have two successful exits—RemoteTeam, which was acquired by Gusto, and MovieLaLa, which Gfycat bought. I’ve also written over 200 angel checks, backing companies like Anthropic, OpenAI, and Scale AI. I’ve learned to spot hype from a mile away, and the hype around AI for reading X-rays and MRIs is deafening.
Everyone seems to think that if you just build a slightly more accurate model for detecting tumors, you’ll build a billion-dollar company. They’re wrong.
The Uncomfortable Truth
Most founders in this space are obsessed with model performance. They spend all their time tweaking algorithms to get another percentage point of accuracy. That’s the easy part. The hard part is everything else.
I remember meeting a founder a few years ago who had a brilliant model for detecting a specific type of cancer from medical images. He’d raised a seed round, had a team of PhDs, and was convinced he was on the verge of revolutionizing healthcare. A year later, the company was dead. Why? He never figured out how to get the software into the hands of doctors. He was so focused on the tech that he forgot about the user.
This is the reality of healthcare. It’s a brutal, regulated, and slow-moving industry. You can’t just airdrop a new technology into a hospital and expect everyone to start using it. You need to integrate with ancient IT systems, navigate a maze of data privacy laws, and convince doctors who have been doing things the same way for 20 years to change their workflow.
Where the Real Opportunities Are
So, if I’m so down on AI in radiology, where am I putting my money? I’m looking for companies that are using AI to solve the unsexy problems in healthcare. The problems that don’t make for flashy headlines but are massive opportunities.
Here are a few areas I’m excited about:
Drug Discovery AI: The process of discovering and developing new drugs is incredibly slow and expensive. AI can change that. I’m seeing companies use AI to analyze massive datasets of genetic and molecular data to identify new drug targets and predict how patients will respond to different treatments. This is a game-changer. We’re not talking about incremental improvements here; we’re talking about a fundamental shift in how we create new medicines.
AI for Mental Health: We have a global mental health crisis, and there aren’t enough therapists to go around. AI can help bridge that gap. I’ve invested in companies that are building AI-powered chatbots and virtual assistants to provide on-demand mental health support. These tools aren’t meant to replace human therapists, but they can provide a crucial lifeline for people who are struggling.
Clinical AI for Workflow Automation: Doctors and nurses are drowning in paperwork and administrative tasks. This is a huge drain on their time and a major contributor to burnout. I’m backing companies that are using AI to automate these tedious tasks, like writing clinical notes, filling out insurance forms, and scheduling appointments. This isn’t as glamorous as finding cancer, but it’s a massive problem that needs to be solved.
My Advice to Founders
If you’re a founder who wants to build a successful AI company in healthcare, here’s my advice: stop chasing the hype. Stop trying to build the most accurate model. Instead, go find a real problem that doctors and patients are facing, and then figure out how AI can solve it.
Talk to your users. Understand their workflow. And be prepared for a long, hard fight. Building a company in healthcare is not for the faint of heart. But if you’re willing to put in the work, the rewards can be immense. You won’t just build a successful company; you’ll have a chance to actually improve people’s lives.
That’s a legacy worth fighting for. '''
Frequently Asked Questions
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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.
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.
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.