MACHINE LEARNING & AI: A HIMSS EVENT

LAS VEGAS, NV - MARCH 5, 2018

HIMSS18 Annual Conference
Wynn Las Vegas
Mar. 5, 2018

Lynda Chin

Executive Director, Real-World Detection and Intervention Platform
University of Texas System

Dr. Chin is an elected member of the National Academy of Medicine and a world renowned cancer genomic scientist who had authored over 200 peer-reviewed publications, and delivered over 300 presentations in national and international conferences. 

Her work spans the fields of transcription, telomere biology, mouse models of human cancers, cancer genomics and personalized medicine, as well as development of novel drug development construct and technology-enabled healthcare transformation for the underserved.  

As a clinically trained physician scientist, Dr. Chin is passionate about improving health and health outcome of patients through science and technology. As an innovator, Dr. Chin is a champion for foundational infrastructure that integrates across research and clinical care domains as well as across industry sectors, because she believes that broad and material impact from science and technology can only be realized through collaboration that needs to be enabled and facilitated by such infrastructure among otherwise independent (public or private) enterprises. Dr. Chin is also an entrepreneur who has founded or co-founded 3 biotechnology and AI companies.

March 5, 2018
8:35am - 9:05am
Lafleur

We are in an age of disruptive technology.  While disruption is much needed in healthcare, transformation will require integration and collaboration.  Technology, including machine learning and AI analytics, is not the solution on its own; rather it is a tool that can enable a new solution.  Therefore, we must not forget the challenges of integrating technology into new or re-designed workflows and training our workforce in new or re-defined roles in order to realize the desired outcome.

In this opening keynote, renowned physician and scientist Lynda Chin takes a hard look at what else is needed for machine learning and AI to move beyond hype and deliver on its promise to transform healthcare.  It is more than just the analytics.  It boils down to needing:

  • Data: AI algorithms need data, lots of it, from clinical and real-world settings, not just a snapshot but longitudinal and contextualized
  • Ground truth:  Machine learning needs ground truth, but black and white “truth” is rare in medicine;
  • Expertise: Training algorithms requires domain expertise, not just technical competence
  • Learning: Algorithms need to evolve based on feedback, better evidence, new knowledge

At the end of the day, algorithms do not treat patients, doctors do. Therefore, as Chin explains, collaboration between the domain experts and the technologists will be necessary to establish the trust and transparency to drive adoption to realize the transformative impact of AI.

March 5, 2018
12:40pm - 1:00pm
Lafleur

One of the best ways learn is to network with your peers. This session will provide an opportunity to meet speakers and attendees who have similar privacy and security challenges and discuss solutions to those challenges.

Here's how it works:

Speakers will be stationed at different tables in the ballroom, and attendees can circulate and speak one-on-one or in groups with individual speakers. The speakers have been assigned different topics, but other topics can also be addressed.

Mingle, share and learn in this interactive environment.

March 5, 2018
9:20am - 9:55am
Lafleur

There’s a ton of buzz around machine learning and artificial intelligence and the role they’ll play in revolutionizing and improving healthcare, but what are these evolving technologies, how are they different, and what are the multiple layers of use?

No complex health IT ever created has been plug-and-play. The same goes for ML and AI. To take full advantage of its potential, will require a lot of work.

In this morning leadership panel, our speakers look at the current state of machine learning and AI in healthcare and address where we are, where we are going, and what we need to do to get their faster.

What education do stakeholders need? What new vocabulary is required? How do you integrate ML and AI into operational and clinical processes? How do you show ROI?

Our speakers will address these questions and others head-on in a fascinating and insightful discussion that sets the stage for the speakers to follow.

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