Kun-Hsing Yu "AI vs MD: Will Machines Replace Doctors?"

DEPARTMENT OF
Biomedical Informatics
MD vs. AI:
Will Machines Replace Doctors?
Kun-Hsing “Kun” Yu, MD, PhD
Department of Biomedical Informatics
Harvard Medical School
December 7th, 2018
1

DEPARTMENT OF
Conflict of Interest Statement
• Harvard Medical School submitted a non-provisional patent
application on digital pathology profiling to the U.S. Patent and
Trademark Office (USPTO)
• Biggest conflict: I am an MD, and I am a big fan of AI since childhood
2

DEPARTMENT OF
Outline
• What’s inside the Artificial Intelligence (AI) black box?
• How will AI transform medicine?
3

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Artificial Intelligence
• The field of study that attempts to both understand and build
intelligent entities
• Fueled by
• Availability of the big data
• Advanced machine learning algorithms
• Growth in computation power
42006
=
2018

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The AlphaGo Saga
• AlphaGo vs. Lee Sedol (4:1)
• AlphaGo vs. 60 professional players (60:0)
• AlphaGo vs. Ke Jie (3:0)
• AlphaGo Zero vs. AlphaGo (100:0)
5
Silver D et al. Nature. 2016 Jan 28;529(7587):484-489.
Silver D et al. Nature. 2017 Oct 18;550(7676):354-359.

DEPARTMENT OF
• A deep neural network model
detects referable diabetic
retinopathy with expert-level
performance
• A similar system by Univ. of Iowa is
approved by the U.S. FDA
6
Gulshan V et al. JAMA. 2016 Dec 13;316(22):2402-2410.
Abràmoff MD et al. npj Digital Medicine. 2018 Aug 28;1:39.

DEPARTMENT OF
• Machine learning models
accurately classified skin
lesion images into 2,032
diagnoses
7
Esteva A et al. Nature. 2017 Feb 2;542(7639):115-118.

DEPARTMENT OF
• Similar approaches
detected tuberculosis
on chest radiographs
with 99% accuracy
8
Paras Lakhani, MD
Baskaran Sundaram, MD
Lakhani P et al. Radiology. 2017 Aug;284(2):574-582.

DEPARTMENT OF
• When was it developed?
• 1975?
• 2012?
• 2017?
• 2018?
9
• An intelligent consultation program
that suggests treatment options for
patients with infectious diseases
• 2.5%-22.5% better than individual
infection specialists

DEPARTMENT OF
• When was it developed?
• 1975!
10
Shortliffe EH and Buchanan BG. Mathematical Biosciences. 1975; 23 (3-4): 351–379.
Dr. Shortliffe
circa 1970s
Dr. Shortliffe in
2010s

DEPARTMENT OF
What’s going on?
¿Qué está pasando?
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DEPARTMENT OF
AI Hype and AI Winter
• Over-inflated expectations → subsequent crash
• Lighthill report (1973)
• DARPA cuts academic AI research (1974)
• Collapse of Lisp machine market (1987)
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How to Avoid the Next AI Winter?
• Understand how AI really works and its limitations
• Address the challenges of implementing AI applications
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What’s Inside the AI Blackbox?
• Rule-based approach
• Machine learning
• Deep learning
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1. Rule-based Approach
• A popular approach in 1970s
• Build a knowledge-base of rules
• Draw conclusions based on the rules
• Limitations
• Need to formulate the inference rules
• Difficult to maintain and update the
knowledge-base
15
Shortliffe EH and Buchanan BG. Mathematical Biosciences. 1975; 23 (3-4): 351–379.
Yu KH et al. Nat Biomed Eng. 2018 Oct 10;2(10):719-731.

DEPARTMENT OF
2. Machine Learning (ML)
• Allows machines to learn non-obvious associations from the data
• e.g., Supervised ML:
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Training Set
{(x(i),y(i)); i=1,…,m}
Machine learning
algorithm
Machine learning
model
New Data
{x’}
Predicted Outcome
{y’}
e.g. the symptoms
of a new patient
e.g. diagnoses
e.g. a comprehensive symptom-
diagnoses dataset identified in
the Partners’ medical record

DEPARTMENT OF
Case Study: A Pathology AI System
• Pathology: the definitive diagnostic method for many cancers
• Performed by trained pathologists
• Defined disease types, but inter-rater disagreement has been reported
• Automated image processing pipelines enables the extraction of objective features
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Yu KH et al. Nat Commun. 2016 Aug 16;7:12474.
Yu KH et al. Cell Systems 2017 Dec 27;5(6):620-627.

DEPARTMENT OF
A Pathology AI System
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Obtained whole-slide histopathology images
Divided into training and test sets
Automated Regions
of Interest (ROI)
Selection
Feature visualization
Features and feature interpretation
Machine
Learning
Yu KH et al. (patent pending); Yu KH et al. (under review)
Deep Learning Model

DEPARTMENT OF
AI Detects Lung Cancer and Predicts Patient Survival
19
Lung cancer subtype classificationTumor cell detection
AUC=0.979-0.995
Top features: Radial distribution of nuclei pixels, Textures (pixel
correlations, intensity variance) of the nuclei
Yu KH et al. (patent pending); Yu KH et al. (under review)
P=0.0023

DEPARTMENT OF
Image Features are Correlated with Genetic Mutation
Status
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log (feature value)
Associated image features:
• Distribution of cytoplasm
intensity
• Texture features of the nuclei
Yu KH et al. Cell Systems 2017 Dec 27;5(6):620-627.

DEPARTMENT OF
Limitations of Machine Learning-based AI
• “Garbage in, garbage out” (GIGO)
• Model generalizability depends on the representativeness of the training data
• Machines are not so “objective” if the input labels are not “objective”
• The labeling of cases could evolve over time
• Correlation, not causation
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Yu KH et al. BMJ Qual Saf. 2018 Oct 5. pii: bmjqs-2018-008551. [Epub ahead of print]

DEPARTMENT OF
Transforming Medicine with AI
22

DEPARTMENT OF
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DEPARTMENT OF
The MD vs. AI Duel?
• Beware of the “superhuman fallacy”
• To contribute to medical practice, AI does
NOT need to beat the best human
practitioner
• A system with “average” level of expertise
can help numerous communities
25

DEPARTMENT OF
Who Benefits from It?
• AI developers
• May profit from the big and profitable market in healthcare
• Healthcare providers
• Can improve efficiency
• Payers
• Can reduce waste from misdiagnoses
• Pharma
• May identify novel treatment strategies using advanced data analytics
• Patient???
26
?

DEPARTMENT OF
Some Challenges of Medical AI Implementations
• Integration into the clinical workflow
• How would clinicians catch AI misdiagnosis?
• Regulatory challenges of AI model
• FDA: the pre-certified approach announced in April 2018
• Interpretation of machine learning models
• How to understand the AI “black-boxes”?
• Socio-legal implications of medical AI applications
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DEPARTMENT OF
Summary
• Many medical AI applications are on the horizon
• But beware of the over-inflated claims and projections
• Machine learning models are as good as the training data
• Need to address biases in the data
• Many social, economical, and legal challenges ahead
• Require open discussions of a broad range of stakeholders
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DEPARTMENT OF
Will AI replace MDs?
• No (at least not in the near future).
• But MDs who use AI will replace those who don’t.
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DEPARTMENT OF
Acknowledgments
• Zak Lab
• Isaac “Zak” Kohane, MD, PhD
• Nathan Palmer, PhD
• Arjun Manrai, PhD
• Ariel Feiglin, PhD
• Daria Prilutsky, PhD
• William Yuan, MChem
• Oren Miron, MA
• Brett Beaulieu-Jones, PhD
• Andrew Beam, PhD
• Sam Finlayson, MS
• Judith Somekh, PhD
• Golden Lab
• Jeffrey A. Golden, MD
• Claudia Rizzini, PhD
• Stanford University
• Michael Snyder, PhD
• Russ B. Altman, MD, PhD
• Christopher Ré, PhD
• Matt van de Rijn, MD, PhD
• Serafim Batzoglou, PhD
• Daniel Rubin, MD, MS
• Gerald Berry, MD
• Feiran Wang, MS
• Funding
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DEPARTMENT OF
Thank you. 
kun-hsing_yu@hms.harvard.edu
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Kun-Hsing Yu "AI vs MD: Will Machines Replace Doctors?"