In the past year, venture capital investments in healthcare artificial intelligence (AI) have surged to nearly $18 billion. However, the realization of a substantial return on these investments remains elusive, with many projects only reaching the experimental phase. Approximately 83% of healthcare systems have initiated generative AI trials, but a mere 5 to 10% of these projects successfully expand to enterprise-wide implementation. Many face obstacles such as procurement challenges, integration hurdles, or limited departmental launches that fail to scale.
Contributors like UCSF Health, Kleiner Perkins, and Doerr Capital believe the issue lies not just with the technology itself but rather with where it's being developed. According to exclusive insights shared with Fortune, they are introducing UCSF Health Converge, an AI accelerator designed with a unique approach: partnering with only two to three companies annually, immersing them within UCSF’s clinical processes, IT frameworks, and operational teams from the outset.
Suresh Gunasekaran, the CEO of UCSF Health, candidly critiques the existing landscape, stating that most solutions coming out of traditional accelerators lack readiness for system-wide implementation. “That final mile is missing,” he explained, indicating a significant gap in the transitional process from pilot projects to widespread adoption.
UCSF’s initiative is not unmatched in the field; Mayo Clinic has successfully accelerated over 70 startups since 2022, providing access to patient data and expert insights in exchange for an equity stake. Similarly, the Cleveland Clinic made headlines last fall by partnering with Khosla Ventures, allowing portfolio companies to test their solutions on actual patients and healthcare providers. UCSF’s stance is clear: the process of developing a product within a healthcare setting is inherently distinct from merely testing it there.
The effectiveness of this new model, however, remains uncertain. Current details about the Converge initiative are sparse, including the absence of a defined equity arrangement or investment specifics from Kleiner and Doerr. Gunasekaran mentioned that the terms are intentionally flexible, tailored to each participating company.
Kleiner Perkins, which recently secured $3.5 billion across two funds with a strong emphasis on AI, will consider investments in Converge participants based on a consensus from its six-member partnership. “It wouldn’t just be up to me,” explained Mamoon Hamid, a partner at Kleiner Perkins, underscoring the collaborative decision-making process.
John Doerr of Doerr Capital, who is co-leading the program alongside Kleiner, acknowledged a pressing issue: 100 million Americans lack access to primary care, and the nation cannot solely rely on training more healthcare professionals to alleviate this crisis. “We have to leverage AI to elevate the roles of everyone within our healthcare teams,” he emphasized.
He draws a historical analogy to Genentech, the pioneering biotech company that originated in the 1970s with close collaboration between scientists and investors within Kleiner Perkins' offices. Doerr believes this form of engagement is vital for catalyzing the healthcare AI movement, stating, “We underestimated the challenge of changing human behavior—patients, caregivers, and specialists alike.” He argues that AI's true potential lies in enabling personalized care at scale, whether by motivating patients to adhere to their treatment plans or implementing seamless documentation systems that free healthcare professionals to spend more quality time with their families.
