The Enterprise Insights Forum is an invitation-only gathering of senior leaders from major healthcare companies—along with select investors, academics, and startup founders—that explores best practices, delivers practical tools, and disseminates useful information to industry leaders around the topic of AI/ML in healthcare. This one-day event will include a diverse series of talks, ranging from fireside chats to panels to skill-based workshops with leaders working at the forefront of healthcare innovation.


Past Speakers



The Enterprise Insights Forum is a one-day, invitation-only gathering of leaders from major healthcare companies along with select investors, academics, and startup founders. In 2018, the focus was built around Rock Health’s recent white paper on AI and machine learning in healthcare, the content was carefully curated with an enterprise perspective in mind. Each year, the Forum will equip you with actionable insights and an expert network to draw upon.



The Forum takes advantage of the intimate size and is structured as an organized un-conference to promote candid and open discussions. You’ll have ample opportunity to engage directly with thought leaders, participate in interactive programming, and ask the tough questions that are top of mind for you and your business.


It’s not just exchanging business cards and a handshake. We’re intentionally keeping this event small and intimate to facilitate meaningful connections with your fellow business leaders, dealmakers, and startup founders. And you can leave your stuffy suit at home—from networking at lunch to a post-event reception, there’ll plenty of opportunity for some fun, too.

what attendees mastered in 2018

Attendees may not have left as an academically trained technologist (that is, unless they came as one), but they were prepared with the tools needed to tackle the most pressing challenges facing their healthcare business in the age of AI. Attendees left the room knowing how to:

  • Internally structure yourself to integrate AI solutions into their businesses
  • Think proactively about the ethical implications of AI specific to their businesses
  • Assess the startup ecosystem and separate hype from practical use cases

This event aimed to support enterprise business leaders—including those at hospitals and health systems, pharmaceutical companies, payers, and technology companies—in navigating and leveraging AI/ML solutions. What’s in it for a company’s specific challenges? Here’s a breakdown of what attendees came away with:

  • Pharma, Life Sciences, and Devices

    • How entrepreneur Iya Khalil of GNS Healthcare is making precision medicine a reality by leveraging causal machine learning for drug discovery
    • Perspective from ZS Associates’ John Piccone on working alongside pharma and med device companies using AI to bend the cost curve
    • A look into GSK’s AI-focused group from John Baldoni on how the company has set itself up for the adoption of AI and successful partnerships with startups
  • Providers

    • How entrepreneurs from leading startups, such as Fabien Beckers of Arterys, work in tandem with physicians to earn their trust and leverage algorithms actively used for patient care
    • Learnings from Dr. Robert Watcher of UCSF about how he selects the right opportunities to plug in ML solutions across his organization—and the impact it’s had so far
    • Strategy for implementing AI-powered bedside workflow tools, shared by Tom Cassels of Leidos Health
  • Payers & Self-Insured Employers

    • Guidance around the practice application of machine learning to optimize risk stratification and map your members to appropriate interventions
    • An approach to creating a framework for addressing unresolved ethical issues as a medical community, from Ian Blumenfeld of Clover Health
  • Tech

    • Thoughts from prominent entrepreneur-turned-venture capitalist Vinod Khosla on how he evaluates potential investments in the crowded AI/ML space
    • A case study from renowned mathematician Gunnar Carlson of tech companies’ role in supporting enterprises in automating previously manual processes using machine learning
    • A playbook of how tech companies are becoming “AI-first”—and why so many are entering the healthcare space
  • Academia

    • How private-public-academic partnerships are addressing the elephant in the room: the need for robust, well-structured, clean data sets to train models
    • An explanation of the gaps and limitations of startups and enterprise companies—and the opportunity for academia to plug in and accelerate the advancement of algorithms, data sets, and full-stack solutions
    • First-hand accounts of creating successful partnerships with startups—such as Brandon Ballinger of Cardiogram and UCSF’s joint effort to study DeepHeart, a deep neural network intended to predict cardiovascular risk