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Dr. Keith Dreyer

Chief Data Science Officer, MGH, BWH, Partners Healthcare Associate Professor of Radiology, Harvard Medical School Chief Science Officer, American College of Radiology Data Science Institute ACR Board of Chancellors, Chairman Informatics Commission

Healthcare AI

2Help sick patients get healthy as soon as possible

Our Role as Physicians

2

1Prevent illness

3Stabilize & manage patients with chronic conditions

Healthcare Spending as a Percentage of GDP (1995-2014) 1999
2015
Institute of Medicine: Improving Diagnosis In Health Care

Radiologist Relevance In Error Reduction

6

Improving the health

of populations

Improving the individual

experience of care

Reducing the per capita

costs of careImproving the work life of those who deliver care Data Science and Artificial Intelligence: A Rapidly Emerging Megatrend in Business and Society Hype? Yes.

Reality?

Yes. Convergence of rapid advances and growth on multiple fronts:

1.Storage and processing power as a cheap, on-demand utility:

Graphics Processing Units (GPUs)

Cloud computing allows affordable GPUs at scale

2.Exponential growth in data to analyze using AI in many fields.

In healthcare:

Medical Imaging

Electronic health records

Genomic data

Patient monitoring and treatment devices (e.g., EKG, Pulse,

Oxygen, IV Pumps, etc..)

Consumer biomonitoring devices (e.g., Fitbit, Apple Watch, smartphones)

High resolution imaging devices

Data registries

Environmental data

Evidence-based guidelines

Clinical trial and other research data

Medical literature and supporting primary data

growth in AI applications in the last 5 years? These technological advances and data growth have spurred:

1.Powerful new applications for known AI techniques

(e.g., deep learning)

2.A global, online community of AI practitioners sharing

advances daily

3.Open source software from the community and tech

4.HugeAI investments from tech titans who see AI as a

strategic asset growth in AI applications in the last 5 years? Data Science is an interdisciplinary field that allows its practitioners to understand and analyze actual phenomena with data. complementary but distinct areas:

1.Mathematics and Statistics

2.Computer science & the sub-fields of artificial intelligence and

machine learning

3.Subject matter knowledge about the analysis topic (i.e., How that

Definition of Data Science

AI H

EALTHCARE

P

UBLICATION

A

CTIVITY

Deep Learning for Health Informatics -Daniele Ravi, et. al., IEEE Journal of Biomedical and Health Informatics, Vol. 21, No. 1,January 2017

Financial Power: Corporate Valuations by Sector

0 100
200
300
400
500
600
700
800

SpeechEHRInsuranceDiagnosticsAI Tech Giants

AMAZON

FACEBOOK

MICROSOFT

GOOGLE

APPLE

PHILIPS

SIEMENS

GE

EPICCERNERNUANCE

MARKET

CAP (BILLIONS AETNA

ANTHEM

UNITED

($3 TRILLION)

NVIDIA

Financial Power: Corporate Valuations by Sector

($3 TRILLION) 0 100
200
300
400
500
600
700
800

SpeechEHRInsuranceDiagnosticsAI Tech Giants

AMAZON

FACEBOOK

MICROSOFT

GOOGLE

APPLE

PHILIPS

SIEMENS

GE

EPICCERNERNUANCE

MARKET

CAP (BILLIONS AETNA

ANTHEM

UNITED

NVIDIA

Data Science, Artificial Intelligence and Healthcare $3.3 Trillion

US HEALTHCARE COSTS

($3.3 TRILLION)

Medical Imaging Expenses

330 Million Individuals

x $10,000 per Person $3 Trillion

Imaging ($300 Billion)

US HEALTHCARE COSTS

($3.3 TRILLION)

Medical Imaging Expenses

10% $3 Trillion

Imaging Acquisition

($270 Billion)

Imaging Interpretation

($30 Billion)

US HEALTHCARE COSTS

($3.3 TRILLION)

Medical Imaging Expenses

<1%

Making imaging safe, effective and accessible

to those who need it. 36K
members75+ years

Core Purpose

To serve patients and society by empowering members to advance the practice, science, and professions of radiological care. 20K digital facilities

The ACR: Leading the Way

23
0 2000
4000
6000
8000
10000
12000
2012
Q4 2013
Q1 2013
Q2 2013
Q3 2013
Q4 2014
Q1 2014
Q2 2014
Q3 2014
Q4 2015
Q1 2015
Q2 2015
Q3 2015
Q4 2016
Q1 2016
Q2 2016
Q3

Registries

Accreditation

Clinical Trials

T R

ANSFER

OF I MAGES A ND D ATA

Imaging Facilities Digitally Connected to ACR

>12,000

Radiologist Relevance In Error Reduction

3 Key Actions:

Imaging 3.0 is a vision and strategy for

providing optimal imaging care.

Imaging 3.0: Value-Based Radiology

28

Culture

Change

Portfolio

of IT Tools

Alignment of

Incentives

AI AI IN C

LINICAL

D

IAGNOSTICS

CLASSIFY

DETECT

AI IN D

IAGNOSTICS

DECISION

EXAM

CLASSIFY

REPORT

VISUALIZE

RESULT

DETECT

Imaging 3.0 is a vision and game plan

for providing optimal imaging care.

Imaging 3.0: Value-Based Radiology

32

Clinical Decision Support for Image Ordering

Providing for millions of examinations per year

3 Key Actions:

Culture

Change

Portfolio

of IT Tools

Alignment of

Incentives

AI IN D

IAGNOSTICS

DECISION

EXAM

CLASSIFY

REPORT

VISUALIZE

RESULT

DETECT

T HE ACR A

PPROPRIATENESS

C

RITERIA

149 AUC Topics

705 AUC Scenarios

6,184 AUC Rules

5,907 Literature References

20 years of continuous work

Hundreds of clinical experts

Multi-specialty based

Rigorous SOE methodology

AHRQ NGC transparency

Continuous updates

Widely referenced

(416) Multidisciplinary Journals

Radiology

1,960

Over 30

Specialties

3,947

ACR AUC

Comprehensive Multidisciplinary Evidence Base

References = 5,907 Articles (416 Journals)

41 (8)Endocrinology

35 (8)Rheumatology

26 (1)Nephrology

21 (1)Genomics

17 (2)Family Medicine

13 (1)Psychiatry

10 (3)Sports Medicine

9 (3)Infectious Disease

9 (4)Pathology

7 (2)Epidemiology

6 (1)Medical Physics

5 (1)Immunology

3 (1)Dermatology

2 (1)Geriatrics

2 (1)Ophthalmology

489 (42)Oncology

447 (38)Surgery

445 (33)Medicine

346 (21)Urology

305 (29)Cardiology

263 (37)Neurology

261 (21)Gynecology

258 (3)Radiation Oncology

181 (28)Gastroenterology

152 (16)Pediatrics

150 (8)Emergency Medicine

121 (6)Nuclear Medicine

115 (13)Orthopedics

110 (11)Pulmonology

50 (13)Vascular

48 (12)ENT

33%67%

5,907 Multidisciplinary Articles

Radiology

(47)

Over 30

Specialties

(369)

11%89%

5,907 Articles

416 Journals

36
EHR I

NTEGRATION

E PIC C

LINICAL

D

ECISION

S

UPPORT

A

NALYTICS

/ F

EEDBACK

C

LINICAL

D

ECISION

S

UPPORT

Imaging 3.0 is a vision and game plan

for providing optimal imaging care.

Imaging 3.0: Value-Based Radiology

38

Clinical Decision Support for Image Ordering

Providing >24 Million examinations per month

Clinical Decision Support for Image Interpretation

Integrated into >75% of radiologists desktops

3 Key Actions:

Culture

Change

Portfolio

of IT Tools

Alignment of

Incentives

AI IN D

IAGNOSTICS

DECISION

EXAM

CLASSIFY

REPORT

VISUALIZE

RESULT

DETECT

L-SPINECLINICALIMAGINGPATHWAY

AI IN D

IAGNOSTICS

DECISION

EXAM

CLASSIFY

REPORT

VISUALIZE

RESULT

DETECT

nonlinear, all-purpose computer system which can be mass-produced by unskilled -Attributed to a 1965 NASA report advocating manned space flight.

Data Defined, Data Designed

Healthcare Solutions

HOW/WHAT

GENERAL

MEDICAL

KNOWLEDGE

INFORMATION

GENERAL

AI

SOLUTIONS

U SING AI IN H

EALTHCARE

Human Defined, Data Designed

Healthcare Solutions

WHYHEALTHCARE?

TOIMPROVEHUMANWELLBEING, REDUCESUFFERING, EXTENDHEALTHYLIFE

HOWTOPERFORMHEALTHCARE?

PREVENT, DIAGNOSEANDTREATDISEASES

THATNEGATIVELYIMPACTHEALTHYLIFE

WHATARESOMEWAYSTODOTHIS?

DISEASEMANAGEMENT(DIAGNOSE/TREAT)

POPULATIONHEALTHMANAGEMENT

WHAT

TRAINEDAI

INFERENCE

MODELS

HOW HUMAN

DEVELOPED

AI USECASES

MULTIPLE

NARROW

AI

SOLUTIONS

FINDTHE REDȁXȂ

H UMAN N ARROW AI D

ETECTING

D

IFFERENT

C OLORS X XX X X X X X X X X X X XXX X XX X X XX X X X X X Xquotesdbs_dbs19.pdfusesText_25