The Radiologist and AI Questions of Economics, Science, and Function - - PowerPoint PPT Presentation

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The Radiologist and AI Questions of Economics, Science, and Function - - PowerPoint PPT Presentation

The Radiologist and AI Questions of Economics, Science, and Function Ari Goldberg MD, PhD Loyola University Chicago What many Radiologists hear: The Reality We are already seeing some impatience with AI and its Radiology adoption In fact,


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The Radiologist and AI

Ari Goldberg MD, PhD Loyola University Chicago Questions of Economics, Science, and Function

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What many Radiologists hear:

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The Reality

We are already seeing some impatience with AI and its Radiology adoption

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In fact, investment fell for first time recently:

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Resistance

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Nevertheless…

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The Radiologist’s Role?

  • So, reality is different from anticipated
  • However

– Large economic drivers for AI advancement persist – Significant upsides to individual practice and large-scale healthcare

  • Unique role/time for Radiologists to affect future care and policy

– Where do we focus?

  • More efficiency or better diagnostic performance?
  • Improve what we do or shift to new paradigm?
  • Data brokers?
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Enhanced productivity

  • Faster reads = More reads

– Lesion detection – Lesion measurement – Triage (X-rays with ptx) – Relevant supporting patient data and EHR

  • Dashboards
  • Automated clinician notification
  • Faster scans

– AI-driven workflow – AI-driven image reconstruction

  • MRI iterative + AI recon schemes
  • CT dose reduction
  • Potential for increases in efficiency in US to have global impact
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Diagnosis / Fewer Misdiagnoses

  • Detection

– Lung nodules – Breast asymmetry

  • “Triage”

– Benign vs Malignant – Acute vs Chronic

  • Bleed
  • Mass
  • Virtual Pathology = most sophisticated

– Adenocarcinoma vs Squamous – Mets vs Primary

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Challenges of Diagnosis

  • Supervised learning

– Machine Learning algorithm in which the system is presented with labelled or annotated examples from which to learn – Partially limited by radiologist guidance and data annotation

  • Example = Training prostate MRI software with PI-RADS scoring from radiologist

– Most common, requires less data

  • Unsupervised and Deep learning

– Machine Learning algorithm must infer inherent structure of the data, grounded with outcomes and pathology

  • Prostate MRI training with pathology only

– Neuronal mimicry to learn how to recognize complex latent patterns in the data

  • Radiologist fear of replacement

– Important to remember: are there fewer or more airline pilots now that we have autopilot?

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Case 1

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Case 2

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“Simplify” with additional demographic and clinical data?.....

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Example from machine vision

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More data = more data

  • As we add data to help distinguish one clinical or imaging

situation from another, we add variables and thus the need for more data sets.

  • Estimated that true diagnosis of lung CA on lung screener

CTs will require ~10 million lung CTs.

  • Data is valuable
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Radiologists as data gatekeepers

  • Radiologists can (and should?) play central role in

stewarding PHI data.

  • Much of technical aspects go beyond hospital admin and

legal

– Type, Relevance, and Volume of data – Method of transit/sharing – Volume – Formatting – Value

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Radiologists involved in projects should know….

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….and also know:

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Can AI take us backward? But in a good way…

  • All of the above relates to making Radiologists better and faster at

what we already do

  • Can AI provide the opportunity for Radiologists to evolve?

– Radiologist as the high-level manager, using integrated approach of images and robust AI to guide disease management? – Less commoditization, more consultant and clinician

  • Breast radiology a template
  • Evolution may be necessary due to other specialties

harnessing aspects of AI

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Radiology + AI

Special Thanks to Drs Khan Siddiqui MD and Orest Boyko MD,PhD