Artificial Intelligence

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in Health Care

Investment in AI health startups

● mainly used in the back

● Clinical use is increasing

● Drug makers are using AI

● Financial costs:

● Privacy and ethical costs:

● Reputational costs:

● Imaging, imaging, imaging:

● A recent example:

● The benefit:

● pump the breaks

● AI might play a role in quality assurance and help surface research

●

● AI comes in many forms

● Two main tasks:

● A third use:

Machine learning:

Deep learning:

Neural network:

Natural language processing:

●schedule its operating rooms

● detect the onset of sepsis

●

target interventions to particular patients

● improving the R&D process

●examine the mechanisms of

disease

●

● helping with recruiting

processing and cleaning of data

● incorporate real-world evidence

● significant roadblocks to overcome

● The goal:

●

● The possibilities are enormous,

●unrealistic expectations

●skepticism and

anxiety

● slower adoption

●built in incentives to aggressively market

the use of AI

● sell their products

●work is cutting-edge to attract patients

●co-branding arrangements

●

● needs to be grounded in facts

● dispel hype, not peddle it.

The Gartner Hype Cycle

Source: www.gartner.com

POLL QUESTION

● two extremes

● replacing doctorscuring terminal disease

● the truth becomes much harder to suss out

● AI is designed to augment human capabilities

● doctors can focus on tasks that matter

● Human oversight will continue to be needed

● Progress is slow and incremental.

● Use in clinical practice remains largely experimental.

●

21st Century Cures Act

●products can be exempt from

regulation if users can “independently review

● has not clarified what it means

● left to determine on their own

● commercialization of IBM’s leading cancer product, Watson for Oncology,

●required to do prospective studies

POLL QUESTION

●not enough to enable AI systems to

generate new insights

● A greater diversity of data is needed.

● Providers use different EHR systems

● harder to link data sets

● Solving this is crucial

●not perfect

●

● data must be properly obtained

● hype is a continued threat

● need for sound science and

prospective studies.

● meaningful partnerships

● long-term vision

rapid increase in AI use

reason for optimism

vigilance will be necessary

clearer picture of winners and losers