In 2018, we dove into the topic of AI and machine learning in healthcare
While AI/ML are critical tools for solving big problems in healthcare, we’re concerned by hype causing confusion in the space, and by solutions being developed without sufficient regard for their efficacy and business value. We built on our white paper, Demystifying AI and Machine Learning in Healthcare, to offer perspective on what the launch point of AI in healthcare really is—where it’s ripe for use, and where we won’t see movement for years to come.
Attendees came away with:
An evaluation framework for AI/ML algorithms
Understanding of the major venture trends in AI/ML-powered digital health companies
Tools to manage major threats to adoption and scale
Practical guidance for overcoming hurdles such as dealing with dumb and dirty data, incorporating AI into an enterprise business strategy, and navigating regulatory uncertainty
A new network of companies and academics with the most promising solutions
past agenda highlights
Is AI Ready for its bedside debut?
A bounty of new studies suggests machine learning algorithms are more effective at diagnosis than physicians are, but machines are not diagnosing real patients—yet. Providers are fairly asking some tough questions of these tools as they relate to care delivery—are they effective, will I get paid, and does this put me at greater risk for malpractice? We’ll discuss these implementation hurdles and lay out the timeline for today’s solutions to fully realize the opportunity they represent for patients and providers—lower costs, faster service, and better treatment.
Emerging ethical issues for healthcare in the age of ai
Despite all their promise, algorithms trained on data which may contain unforeseen omissions or biases can result in subtly—and perhaps dangerously—biased predictions. In this session, we’ll explore the ethical issues algorithms pose, including: what margin of error will be allowed for computers in healthcare? What rights do patients have with regards to the data and algorithms involved in their care? Experts will advise on how to proactively surface and mitigate bias, as well as offer a framework for addressing unresolved ethical issues as a community.
The enterprise playbook for identifying breakthrough innovation
It has never been more crucial for enterprise to identify which startups are both optimally using AI/ML and ready to scale within healthcare. Yet the path forward for effective integration between startup technology and enterprise business lines is overwhelming—leaders must decide which type of contract is most helpful in managing “readiness risk,” disentangle the different validation signals—from the FDA to academic studies— to assess a startup’s potential to scale, and evaluate compatibility with their own internal data stack and workflows. Investors and enterprise leaders will discuss how companies should navigate the AI/ML startup space in order to differentiate between hype and real business value.