Uncertainty in AI

Uncertainty
in AI
Maarten van Smeden, PhD
Julius Center for Health Sciences
and Primary Care, UMC Utrecht
SIDM The future of diagnosis
Utrecht, 4 July 2023

Disclosures
`
Nothing to disclose
https://bit.ly/2N4mQFo; https://bit.ly/2W7X9rF

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

https://twitter.com/AndrewLBeam/status/1620855064033382401?s=20&t=VO9_LdFFCj_wcwIQLvKcIQ

Source: Ilse Kant (UMC Utrecht)

Utrecht, 4 July 2023 Twitter: @MaartenvSmeden
https://bit.ly/2TOdd0F

https://bit.ly/2v2aokk

Ayers, JAMA Int Med, 2023, doi: 10.1001/jamainternmed.2023.1838
*Answers by healthcare professionals on Redit vs ChatGPT

Source: https://twitter.com/TansuYegen/status/1635388676539813889?s=20

Source: https://www.science.org/content/article/alarmed-tech-leaders-call-ai-research-pause

Examples where
AI has done well

Proportion of studies indexed in Medline with the Medical Subject
Heading (MeSH) term “Artificial Intelligence”
Faes et al. doi: 10.3389/fdgth.2022.833912

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
• Sensitivity and specificity > .90

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

Examples where
AI has done poorly

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

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

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

Uncertainty
#1: has it
been well
developed?

Example: living review
COVID-19 prediction models
• 731 prediction models between
March 2020 and February 2021
• Many models poorly reported
• Only 4% low risk of bias

External validation
COVID-19 prediction
models

“55.6% studies made recommendations
For clinical use in their main text without
any external validation”
“84% of developed models … were at
overall high risk of bias.”

Uncertainty
#2: has it any
chance to
become
implemented?

Development of the AI is only one element
Source: https://tinyurl.com/jr23pdsk; courtesy Dr Ilse Kant (UMCU)

Pipeline of algorithmic medicine failure
Van Royen et al, ERJ, 2922, doi:10.1183/13993003.00250-2022, also credits to Laure Wynants

Uncertainty #3:
is it sufficiently
safe and
effective to be
used?

Guideline diagnostic/prognostic applications of AI
https://www.leidraad-ai.nl/

Collection and
management of the
data
Phase 1
Development of the
AIP
Phase 2
Validation of the
AIPA
Phase 3
Development of the
required software
Phase 4
Impact assessment
of the AIPA in
combination with
the software
Phase 5
Implementation
and use of the AIPA
with software in
daily practice
Phase 6
Saskia Haitjema
Andre Dekker
Paul Algra
Amy Eikelenboom
Christian van
Ginkel
Martine de Vries
Daniel Oberski
Desy Kakiay
Kicky van
Leeuwen
Joran Lokkerbol
Evangelos
Kanoulas
Gabrielle
Davelaar
Wouter Veldhuis
Bart-Jan Verhoeff
Vincent Stirler
Daan van den
Donk
Huib Burger
Giovanni Cina
Martijn van der
Meulen
Maurits Kaptein
Floor van
Leeuwen
Egge van der Poel
Marcel Hilgersom
Sade Faneyte
Jonas Teuwen
Teus Kappen
Ewout Steyerberg
Leo Hovestadt
René Drost
Bart Geerts
Anne de Hond
René Verhaart
Nynke Breimer
Karen Wiegant
Laure Wynants
Lysette
Meuleman

Collection and
management of the
data
Phase 1
Development of the
AIP
Phase 2
Validation of the
AIPA
Phase 3
Development of the
required software
Phase 4
Impact assessment
of the AIPA in
combination with
the software
Phase 5
Implementation
and use of the AIPA
with software in
daily practice
Phase 6
Saskia Haitjema
Andre Dekker
Paul Algra
Amy Eikelenboom
Christian van
Ginkel
Martine de Vries
Daniel Oberski
Desy Kakiay
Kicky van
Leeuwen
Joran Lokkerbol
Evangelos
Kanoulas
Gabrielle
Davelaar
Wouter Veldhuis
Bart-Jan Verhoeff
Vincent Stirler
Daan van den
Donk
Huib Burger
Giovanni Cina
Martijn van der
Meulen
Maurits Kaptein
Floor van
Leeuwen
Egge van der Poel
Marcel Hilgersom
Sade Faneyte
Jonas Teuwen
Teus Kappen
Ewout Steyerberg
Leo Hovestadt
René Drost
Bart Geerts
Anne de Hond
René Verhaart
Nynke Breimer
Karen Wiegant
Laure Wynants
Lysette
Meuleman
Traditionally, most
attention goes to

Uncertainty
#4: Remaining
challenges

Some remaining questions and challenges
• How do we ensure implemented AI models remain safe and
effective over time and place?
• How do we ensure AI models are not leading to disadvantage
for certain groups of patients?
• How do we let the AI tell us when a diagnosis is uncertain?
• How do we increase trust and transparency of AI models?
Model updating, monitoring, transfer learning
Algorithmic bias, fairness
Conformal prediction
Trustworthy AI, explainable AI

https://www.umcutrecht.nl/en/campaign/ai-labs

Closing remarks
AI is here, and will be part of the future of diagnosis
Implementing AI in healthcare and prevention comes with
some unique (and some less unique) challenges:
the uncertainties of AI
Guidance available on the whole trajectory from concept to
implementation

Email: M.vanSmeden@umcutrecht.nl
Twitter: @MaartenvSmeden
https://www.umcutrecht.nl/en/campaign/ai-labs
Slides available @ https://www.slideshare.net/MaartenvanSmeden/

Uncertainty in AI

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