Five questions about artificial intelligence

Maarten van Smeden, PhD
Explainable AI workshop
12 April 2021
Five questions about AI in medicine

Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Conflicts of interest
Financially
• I do not own (any) patents or stocks, and am I not involved in the
development of any Artificial Intelligence (AI) related products
• I am not paid for this talk
• I am involved in the development of a field standard for medical
AI, commissioned by the Dutch government, for which a financial
compensation was granted
Intellectually
• I am a statistician
• In interviews and on social media I have been quite sceptical
about AI (hype) in medicine
• Overall, I believe the interest in AI in medicine is net-beneficial
for someone in my position, although…

https://bit.ly/2CwW43A

https://bit.ly/2TOdd0F

https://bit.ly/2v2aokk

Some general observations about AI in medicine
• Incredibly hot
• Incredibly heterogeneous
• Robots, data analyses, self-learning systems,…
• Types of data
• “Traditional” structured data
• Medical imaging
• Gene expression data
• Text mining electronic health records
• Analyzing social media posts (e.g. pharmacovigilance)
• Speech signal processing (e.g. )
• Incredibly opaque
• Limited information about actual use of AI in healthcare
• Almost no regulations (yet)

Tech company business model

Tech company business model
https://bit.ly/2HSp8X5; https://bit.ly/2Z0Pfop; https://bit.ly/2KIcpHG; https://bit.ly/33IJhr9

Other success stories
https://go.nature.com/2VG2hS7; https://bbc.in/2Z1drXQ; https://bit.ly/2TAfRIP

IBM Watson winning Jeopardy! (2011)
https://bbc.in/2TMvV8I

IBM Watson for oncology
https://bit.ly/2LxiWGj

Example: retinal disease
Gulshan et al, JAMA, 2016, 10.1001/jama.2016.17216; Picture retinopathy: https://bit.ly/2kB3X2w
Diabetic retinopathy
Deep learning (= Neural network)
• 128,000 images
• Transfer learning (preinitialization)
• Sensitivity and specificity > .90
• Estimated from training data

Example: lymph node metastases
Bejnordi et al, JAMA, 2018, doi: 10.1001/jama.2017.14585. See our letter to the editor for a critical discussion: https://bit.ly/2kcYS0e
Deep learning competition
But:
• 390 teams signed up, 23 submitted
• “Only” 270 images for training
• Test AUC range: 0.56 to 0.99

AI is everywhere
https://bit.ly/2ka0HLq; https://go.nature.com/33TQgO6; https://bit.ly/2kp6X23; https://bit.ly/2lZuKWt; https://bit.ly/2lI298g

“As of today, we have deployed the system in 16 hospitals, and
it is performing over 1,300 screenings per day”
MedRxiv pre-print only, 23 March 2020,
doi.org/10.1101/2020.03.19.20039354

Living review (update 3)
doi: 10.1136/bmj.m1328

Living review (update 3)
Risk of bias assessment ursing PROBAST tool: https://www.probast.org/
doi: 10.1136/bmj.m1328

5 questions about AI in medicine
1. Is AI truly intelligent?
2. Is AI old statistics wine in new machine learning bottles?
3. Is AI able to explain?
4. Surely, AI is better at making predictions?
5. Will AI make healthcare better, faster and cheaper?

Q1: Is AI truly intelligent?

Turing, Mind, 1950, doi: 10.1093/mind/LIX.236.433

Source: https://openai.com/blog/multimodal-neurons/

claiming that a classifier trained on
zillions of human-labelled images
containing cats and no cats, is
recognizing cats is just stupid – a
human can see a handful of cats,
including cartoons of pink panthers,
and lions and tigers and panthers, and
then can not only recognize many
other types of cats, but even if they
lose their sight, might have a pretty
good go at telling whether they are
holding their moggy or their doggy
https://bit.ly/326ghK8
Jon Crowcroft

Adversarial example
https://bit.ly/2N4mQFo; https://bit.ly/2W7X9rF

Skin cancer and rulers
Esteva et al., Nature, 2016, DOI: 10.1038/nature21056; https://bit.ly/2lE0vV0

https://arxiv.org/abs/2008.07371

All the impressive achievements of
deep learning amount to just curve
fitting
https://bit.ly/3t8kLfl
Judea Pearl

Q2: is AI old statistics wine in
new machine learning bottles?
AI
100%
linear
models

Terminology
In medical research, “artificial intelligence” usually
just means “machine learning” or “algorithm”

https://bit.ly/38A1ng0

“Everything is an ML method”
https://bit.ly/2lEVn33

“ML methods come from computer science”
https://bit.ly/2zhbwPv; https://stanford.io/2TVp1xK; https://stanford.io/2ZfED0k
Leo Breiman Jerome H Friedman Trevor Hastie
CART, random forest Gradient boosting Elements of statistical learning
Education Physics/Math Physics Statistics
Job title Professor of Statistics Professor of Statistics Professor of Statistics

Two cultures
Breiman, Stat Sci, 2001, DOI: 10.1214/ss/1009213726

Statistics Machine learning
Covariates Features
Outcome variable Target
Model Network, graphs
Parameters Weights
Model for discrete var. Classifier
Model for continuous var. Regression
Log-likelihood Cross-entropy loss
Multinomial regression Softmax
Measurement error Noise
Subject/observation Sample/instance
Dummy coding One-hot encoding
Measurement invariance Concept drift
Statistics Machine learning
Prediction Supervised learning
Latent variable modeling Unsupervised learning
Fitting Learning
Prediction error Error
Sensitivity Recall
Positive predictive value Precision
Contingency table Confusion matrix
Measurement error model Noise-aware ML
Structural equation model Gaussian Bayesian network
Gold standard Ground truth
Derivation–validation Training–test
Experiment A/B test
Adapted from Daniel Obserski: https://bit.ly/2YN12Xf and Robert Tibshirani: https://stanford.io/2zqEGfr
Language

Robert Tibshirani: https://stanford.io/2zqEGfr
Machine learning: large grant = $1,000,000
Statistics: large grant = $50,000

ML/AI refers to a culture, not to methods
Distinguishing between statistics and ML/AI
• Substantial overlap methods
• Substantial overlap analysis goals
• Attempts to distinguish frequently results in disagreement

Beam & Kohane, JAMA, 2018, doi : 10.1001/jama.2017.18391

Q3: Is AI able to explain?
BLACK BOX
INPUT EXPLANATION

Explanatory models
• Theory, cause and effect
• aetiology of illness
• effect of treatment
Prediction models
• Interest in (risk) predictions of future observations
• Cause and effect not a direct concern
• prognosis and diagnosis
Descriptive models
• Capture the data structure

The Basketball thought experiment

The Basketball thought experiment
Relation of interest:
player height -> player talent (“got game”)
Third variable: professional basketball player
CONFOUNDER?

Red = professional, black = amateur basketballer

Red = professional, black = amateur basketballer
• The third variable professional basketball player is a collider
• An algorithm should not control for this collider (as one should
do for a confounder)
• How should an algorithm know it should ignore “professional
basketball player”?
It cannot know based on the data alone!

AI and causal inference
1See further: Kreiff and Diaz Ordaz; https://bit.ly/2m1eYdK
Small selection1
• Superlearner (e.g. van der Laan)
• High dimensional propensity scores (e.g. Schneeweiss)
• The book of why (Pearl)

• Understanding cause and effect crucial in understanding
aetiology, effect of interventions -> explanatory modelling
• There is a large difference between explaining why the AI is
predicting what it is predicting (e.g. feature importance) and the
ability of AI to “truly explain” -> separate causes from effects
• Explanatory modelling is already challenging in structured data

Q4: surely, AI is better at making predictions?
Img: https://bit.ly/3saKFO7

Reviewer #2

Systematic review clinical prediction models
Christodoulou et al. Journal of Clinical Epidemiology, 2019, doi: 10.1016/j.jclinepi.2019.02.004

Sources of prediction error
Y = 𝑓 𝑥 + 𝜀
For a model 𝑘 the expected test prediction error is:
σ!
+ bias! -
𝑓" 𝑥 + var -
𝑓" 𝑥
See equation 2.46 in Hastie et al., the elements of statistical learning, https://stanford.io/2voWjra
Irreducible error Mean squared prediction error
(with E 𝜀 = 0, var 𝜀 = 𝜎!
, values in 𝑥 are not random)
What we don’t model How we model
≈
≈

Irreducible error in medicine is often large
• Health and lack thereof complex to measure (‘no gold standard’)
• Predictors of diseases are often imperfectly and partly
measured
• We often don’t know all the causal mechanisms at play
• much easier to predict if you know the causal mechanisms!
• Predicting the future even more difficult
Understanding prediction uncertainty is key
Courtesy Cecile Janssens: https://bit.ly/2Jf5ft6

Classifier Technology and the Illusion of Progress
Hand, Stat Sci, 2006, doi: 10.1214/088342306000000060
David Hand

Predicting mortality – the conclusion
PlosOne, 2018, DOI: 10.1371/journal.pone.0202344

Predicting mortality – the results
PlosOne, 2018, DOI: 10.1371/journal.pone.0202344

Predicting mortality – the media
PlosOne, 2018, DOI: 10.1371/journal.pone.0202344; https://bit.ly/2Q6H41R; https://bit.ly/2m3RLrn

Q5: will AI make healthcare faster, better, cheaper?
Img: https://bit.ly/3wOv0aH

Better?

Faster?
https://dl.acm.org/doi/abs/10.1145/3313831.3376718

Cheaper?
The costs of running (cloud computing) the Transformer
algorithm are estimated at 1 to 3 million Dollars
https://bit.ly/33Dj38X

Flexible algorithms are data hungry
From slide deck Ben van Calster: https://bit.ly/38Aqmjs

https://twitter.com/DrHughHarvey/status/1230218991026819077

When used right, AI will able to do amazing things
… while being subject to many of the same issues of traditional
prediction modelling, including the leaky implementation pipeline

Recidivism Algorithm
Pro-publica (2016) https://bit.ly/1XMKh5R

• Algorithms are not designed to automatically encourage equitable
healthcare and/or fair medical decision making
• Often we seem unaware of selection mechanisms in our data,
poorly reflecting society, enlarging existing inequalities or both
All photos of scientists I used in this presentation were white men

Email: M.vanSmeden@umcutrecht.nl
Twitter: @MaartenvSmeden

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