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Andrew Hartz MD
Michael Romano M4 MD/PhD program
Jonathan Scalera MD

Nothing Artificial About This Man

 Look deep into nature and then you will understand everything better Einstein 

Overview

Radiology residents are taught the theories and physics concepts supporting MRI, CT, radiography, and other medical imaging technologies. AI is rapidly becoming the newest imaging technology used in radiology practices. The goal of this project is to design a curriculum for educating radiology residents about AI.

The current state of AI in Radiology: A Survey

Image Enhancement

Triaging

Segmentation

Third Reader

  • Increasing speed

    • Similar to humans, the performance of deep learning networks continue to improve as it trains on more data.

Natural Language Processing

Research

  • Finding new areas of research

    • Deep learning algorithms are able to identify patterns outside of human perception. New areas of interest can be discovered using deep learning.

  • Curating articles, for example: Scaling up data curation using deep learning: An application to literature triage in genomic variation resources
  • Improving healthcare systems and limitations

    • AI has the potential to improve the workflow in radiology. Increased complexity of AI application can also hinder the workflow. Furthermore, there is the increased costs associated with implementing AI systems that must be considered.


Curriculum

Theory

Problem-based learning

  • How to gather important findings from a chest radiograph?
  • Chest X-Ray Reader/Chester AI
  • How to create a trained neural network using a database of thousands of radiographs?
  • Building a trained neural network

Limitations


Education

Books


Projects

BMC Radiology – Chest X-ray Reader

https://ai.thecommonvein.net/chest-x-ray-reader/

Created by Andrew Hartz, MD and Michael Romano, MS4 MD/PhD Program
Adapted from Chester the AI Radiology Assistant (https://mlmed.org/tools/xray/)

Use with Google Chrome (JavaScript enabled) for best results. Allow the program to load (up to 2 minutes) after uploading any chest x-ray image. Results will be shown on a probability table and saliency map.

Research

AI in Medicine

AI in Radiology

Boston University Resources

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News

Imaging Informatics

  • Society for Imaging Informatics in Medicine
  • ACR Data Science Institute Resources
  • https://acrbulletin.org/acr-bulletin-january-2017/1017-about-machine-learning
  • https://acrbulletin.org/acr-bulletin-may-2017/1167-machine-deep-learning-main
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3372692/
  • http://car.ca/en/education/resources-tools/artificial-intelligence.aspx
  • https://unsupervisedmethods.com/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524
  • http://www.ajronline.org/doi/full/10.2214/AJR.16.17224
  • https://siim.org/page/emimi_group
  • https://kjronline.org/DOIx.php?id=10.3348/kjr.2017.18.4.570
  • http://www.newyorker.com/magazine/2017/04/03/ai-versus-md
  • http://jamanetwork.com/journals/jama/fullarticle/2588764
  • https://www.pedrad.org/LinkClick.aspx?fileticket=cyzAZ9hlLCk%3D&portalid=5
  • https://acrbulletin.org/departments/blog/1345-harnessing-artificial-intelligence-ai-do-deep-learning-machines-still-need-human-radiologists
  • http://www.radiologyleaders.org/Resources/Leadership-Insider-Podcast-Episode-108-Artificial-Intelligence
  • http://www.radiologyleaders.org/resources/leadership-insider-podcast-episode-109-artificial-intelligence-part-two
  • https://soundcloud.com/theaipodcast/ep-12-ai-podcast-how-ai-can-improve-the-diagnosis-and-treatment-of-diseases
  • https://www.youtube.com/watch?v=Gigd1rkZTSE&amp;t=587s

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