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AI & Health

AI & Health

Eighty Arc Fusion leaders joined us for a wonderful evening of talks, dance, film, and a dinner produced by Dosa, the famed South Indian restaurant in San Francisco. Our group broke up into eight separate tables to hold “Jeffersonian Dinners” to discuss the question “What are your greatest hopes and fears about A.I. And A.I. and health?”

Arc Fusion Dinner
San Francisco
May 18, 2016

AI & Health

Eighty Arc Fusion leaders joined us for a wonderful evening of talks, dance, film, and a dinner produced by Dosa, the famed South Indian restaurant in San Francisco. Our group broke up into eight separate tables to hold “Jeffersonian Dinners” to discuss the question “What are your greatest hopes and fears about A.I. And A.I. and health?”

What people said

You go through three phases in addressing revolutionary, exponentially growing information technologies. One is ecstasy at the potential to solve these age-old problems, and then despair at the potential destructive power of these technologies. And ultimately the third phase… is understanding that it is the nature of being human to make progress and overcome problems and risks.

Ray Kurzweil
Google, Author

The types of areas that A.I. should learn include: how do we notice patterns like reading CAT scans in lung cancer so we spot people in phase one [stage of cancer], two and three versus four? How do we find the patterns of women who could take easy preventable actions that could prevent cervical cancer…?

Kevin Noble
Genentech

Computers have learned to do things which previously were just sci-fi. So for example computers have learned how to recommend replies for you, and if you use inbox by Google, you will see this feature where the email will actually suggest possible replies to your email after reading it automatically.

Jeremy Howard
Enlitic

We think there's things that people do terrific that can't be replicated or mimicked by any machine: self directed goals, common sense, value judgment. And there's things that machines do terrifically that humans could never touch upon on a massive scale, things like large scale math pattern discovery and statistical reasoning. So together we think there's a power of the collaboration, an ability to elevate decision making to a new level.

Lori Fiber

AI talent and proprietary data sets will be the new oil and gold that wars are fought over, and we're already seeing this balkanization, sort of this acronym, this FAGMA if you will. Facebook, Apple, Google, Microsoft, Apple, these are the new superpowers battling it out for these. They're sucking up every AI talent out there. They're sucking up all the proprietary data sets because they are the new superpowers.

Tim Chang

Program

 

Arc + IDEO “Framework” on AI & Health

Draft Deck of Arc+IDEO “Framework for the Future” on A.I. & Health
The final version will be finished soon.

Table Discussion and Ideas

Our dinner guests split up into eight tables where everyone answered a table talk question guided by a table talk leader:

What are your greatest hopes and fears about A.I. And A.I. and Health?

To answer this question yourself, and participate in the evenings' polls, click here.
Thanks to our table talk scribes who recorded the conversations at their table each in their own unique style.

Table 1

Jane Metcalfe
Table Talk Leader

IDEO Facilitator: Andrew Lovett-Barron

During our discussion, here are a a few things that popped out at a high level.

  • The idea of repping patient needs. Could ML systems service to identify and represent patience perspectives/advocate for patients?
  • The idea that things are just starting, but everyone is underestimating the difficulty of building this stuff. Healthcare infrastructure around data is going to seriously get in the way.
  • Technology can suck the profit out of unoptimized industries. The question is whether that will be reinvested in getting to good outcomes. Worthwhile question to explore.
  • We know a lot less than we claim to know, and the combination of people NOT understanding AI and NOT understanding wicked healthcare problems from cancer to nutrition is going to be a problem.

Hopes Most of our hopes were centered on patient care and outcomes.

  • AI would finally put the patient squarely at the center of the equation, unifying all records and capturing all relevant data so that care providers are always working with complete information
  • AI could lead to a democratization of healthcare, surfacing the best science and treatments from around the world and making them available for all
  • Perhaps there would evolve non financial incentives for treatment
  • Serve as physician support, not replacement
  • Generate medical and scientific breakthroughs by identifying patterns not see before
  • Avoid mistakes, increase efficiency, lower costs
  • Evolve into predictive models so patients don’t end up sick in the first place, and preventative

Fears

  • Organizations at the table are insurance companies, hospitals, doctors associations and pharma – patients are not at the table – entrenched interests solidify their positions
  • Crap software, bad data lead to bad or unreliable outcomes
  • Lack of qualified personnel
  • Only the rich have access to AI medicine, aggravating the gap between rich and poor
  • Robots replace doctors
  • Dominated by short term/financial goals more than patient outcomes or public health

Gaps

  • Software competence
  • Sufficiently educated workforce
  • Nutrition – we know NOTHING about nutrition and the role it plays in disease (or as Ethan put it, we know SHIT SHIT SHIT about nutrition!)
  • Microbiome – also unknown the role it plays, but AI can help us learn
  • Bad data – how to we make sure we’re capturing good data to begin with
  • HPPA interferes with our ability to collect good data

Table 2

Rodrigo Martinez
Table Talk Leader

IDEO Facilitator: Farzad Azimpour

Rodrigo Martinez – Veritas:

  • Healthcare is manageable
  • Research not Development
  • How to engage people
  • Wish I could have genetics and microbiome data integrated into my care

Comments by others at the table:

Investor:

  • When we talk humans we assume we’re talking about the best humans
  • Human intelligence has not been leveraged
  • You need to have humans! We can’t have isolated conversations.
  • Mental health would be better served with AI since humans just don’t “get it”.

IDEO Designer:

  • Hope that AI gets people off the plate so docs can focus on care
  • Augmented intelligence
  • Enhance visibilities provide uninhibited value

Researcher:

  • Make the “hassle map” visible
  • Fear that anti-bodies will kill the system of AI.

Payer:

  • Won’t think of ramifications of all that data
  • Start small and roll it out. But people will get in the way of effective roll outs.

Physician:

  • I want to see a system that makes people care about their health so they can engage and prevent health problems.

Table 3

Susan Feeny
Table Talk Leader

IDEO Facilitator: Grace Hwang

Notes on the dinner conversation below:

  • Starts with physical exam – who does this? How does AI make this better?
  • Applications driven by incentives and structure
  • Need Education, has not remade public system
  • Interdependent, Rigid > Modular, Felexible
  • Health incentive now: reward cost not value
  • Need data reform
  • Firms can’t make money by treating value – More efficient providers are paid less – 3 cents on dollar wasted
  • Opportunity for AI
  • Race to the bottom, need to create incentives

Table 4

Paul Saffo
Table Talk Leader

IDEO Facilitator: Peter MacDonald

Notes on the dinner conversation to come.

Table 5

Atul Butte
Table Talk Leader

IDEO Facilitator: Grant Wedner

Notes on the dinner conversation to come.

Table 6

Christine Mason
Table Talk Leader

IDEO Facilitator: Kevin Ho

Notes on the dinner conversation to come.

Table 7

Bruce MacGregor
Table Talk Leader

IDEO Facilitator: Remy Fisher

Notes on the dinner conversation to come.

Table 8

Kevin Stone
Table Talk Leader

IDEO Facilitator: Hanah Williams

Notes on the dinner conversation to come.

Arc Fusion Survey on AI & Health

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