A new perspective piece from Flare Capital Partners argues that healthcare AI winners will be determined less by model novelty and more by deployment readiness—especially integration into clinical workflows, proprietary dataset creation, and clear go-to-market execution. The report emphasizes that companies must prove practical utility rather than just technical performance. It highlights the importance of proprietary datasets for building defensible performance in real clinical environments, where external datasets can degrade accuracy. It also calls out the need to determine how technology sells to specific buyers and users, not just how models perform. For the biotech and medtech adjacency audience, the message is operational: evaluation criteria for AI investments are shifting toward execution details that reduce adoption friction in hospitals and specialty practices.