BE PART OF THE REVOLUTION TRANSFORMING HEALTHCARE WITH AI - - PowerPoint PPT Presentation

1000 ATTENDEES 80 SPEAKERS 10 WORKSHOPS 2 SOCIAL EVENTS 1 AIMed19

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BE PART OF THE REVOLUTION

TRANSFORMING HEALTHCARE WITH AI

CALIFORNIA — THE RITZ-CARLTON, LAGUNA NIGUEL 11–14 DECEMBER 2019

CIO/CxO Workshop AI Implications in Healthcare

John Henderson, Vice President & CIO CHOC Children’s Company Name

www.aimed.events/northamerica-2019/

AIMed NORTH AMERICA, CALIFORNIA 11–14 DECEMBER 2019

• Poor quality input may produce unexpected or erroneous AI output

Is Is Data ta In Inte tegrity ity Imp Importa tant? t?

www.aimed.events/northamerica-2019/

AIMed NORTH AMERICA, CALIFORNIA 11–14 DECEMBER 2019

• What percentage of medical records have errors?

70%

Ap Apply lyin ing AI AI & Machin hine e Lea Learnin ing to

• Clin

linic ical l Ca Care & O & Ope perations s

• Pre-authorizations
• Payments
• Coding efficiencies
• Readmission Risk
• Early sepsis predictor
• Drug diversion

Ap Applications of f AI AI in Healthcare

AI AI Use se Cases ses in Hea Healthcare

• AI-Enabled Diagnostic Imaging Interpretation
• Deep Learning Algorithms
• Virtual Personal Health Assistants
• Augmented reality, cognitive computing, sentiment

analysis, and speech and body recognition to create a virtual encounter

• AI for Virtual Care Monitoring and Real-Time Operations

Management

• Predictive and prescription alerting drive decisions for

improved outcomes

AI AI Use Cases fo for Healthcare

Th The Ethics cs of f Ar Artifi fici cial Intelligence ce

Key Considerations:

• Bias can relate to race, gender, age, location or time.
• It can also favor a specific data structure or a specific problem to solve.
• Bias is a natural effect of learning. Eliminating it is no possible.
• Approach it by using the bias and target creation of multiple algorithms

to adjust for the bias you want to avoid

• Trustworthy results come from a diversity of algorithms working on the

same problem

• Some forms of bias may be desirable — for instance, when

determining your values as part of the learning process i.e., wanting to avoid bad language and favoring empathic, polite and patient language are forms of bias toward what you rightly think is important for a conversation or content you want delivered or in moderated conversations between AI-enabled systems and people

• Data for AI often contains incomplete or biased information because

data sources are insufficiently diverse.

Th The C Case se f for Eth Ethics i s in A n AI