"As artificial intelligence becomes a buzzword in nearly every healthcare startup pitch, investors are finding it increasingly challenging to distinguish which ones are actually worth the hype. That's why, during a Thursday panel discussion among venture capitalists at the MedCity INVEST Digital Health conference in Dallas, this question was posed: What metrics do you want to see founders highlighting more often when they're pitching, and what is one red flag that makes you question the validity of their technology? The session was moderated by Neil Patel, head of ventures at Redesign Health."
"For Maddie Hilal, investor at Oak HC/FT, it's important that startups have strong net revenue retention, which measures a company's ability to retain revenue from existing customers. "If we don't necessarily have visibility into those hard [profit and loss] impact proof points, but your existing customer base is growing their contracts, clearly they're excited," she said. "They're seeing the value.""
"Another investor looks for companies with high quality data. "If you have better, higher quality data, you can solve problems in a much better fashion, [with] higher predictability of models. I think we look for that. What's that proprietary data set? What are you trained on? Who and in which environment has this been deployed?" said Rohit Nuwal, partner at TELUS Global Ventures."
Investors increasingly struggle to separate genuine AI solutions from hype in healthcare startups. Strong net revenue retention signals that existing customers are expanding contracts and deriving tangible value. Proprietary, high-quality data improves model predictability and requires clarity about training sets and deployment environments. Demonstrable clinical impact is now a crucial metric alongside reimbursement and financial considerations. Investors look for hard profit-and-loss impact proof points or observable contract growth as validation of product effectiveness. Red flags include vague validation, unclear data provenance, lack of real-world deployment details, and reliance on the "AI" label without measurable outcomes.
Read at MedCity News
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