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Artificial Intelligence
The editorial content in this presentation was written and produced by STAT with no participation from sponsors.
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
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