AI and Healthcare Why Most AI Startups in Healthcare Fail
Jul 18, 2025
Dr. Bernardo Perez-Villa, Senior Innovations Engagement Partner at Cleveland Clinic, sheds light on why many AI startups in healthcare struggle. He emphasizes the need to validate AI ideas against real clinical needs and stresses the importance of product-market fit. The conversation delves into the challenges of regulatory hurdles and income generation in the fee-for-service model. Bernardo shares insights on responsible AI use, data interoperability, and a blockchain-inspired solution for patient data ownership, alongside key principles for successful healthcare innovation.
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Validate Unmet Clinical Need
- Focus on solving an unmet clinical need to achieve product-market fit.
- Validate the problem with at least 10 initial customers who are willing to pay.
Use Primary and Secondary Research
- Conduct primary market research by talking to potential users without pitching your solution first.
- Use secondary research like literature and social media to confirm if the problem is widespread.
Regulatory and Reimbursement Challenges
- Healthcare's fee-for-service model makes startup profitability hard due to slow reimbursement.
- Startups face a costly regulatory maze and must balance cost, quality, and time carefully.
