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Building AI models for healthcare (ML Tech Talks) technology ai

In this session of Machine Learning Tech Talks, Product Manager Lily Peng will discuss the three common myths in building AI models for healthcare.

0:00 – Introduction
1:48 – Myth #1: More data is all you need for a better model
6:58 – Myth #2: An accurate model is all you need for a useful product
9:15 – Myth #3: A good product is sufficient for clinical impact
12:19 – Conversation with Kira Whitehouse, Software Engineer
34:48 – Conversation with Scott McKinney, Software Engineer

Deep Learning for Detection of Diabetic Eye Disease: Gulshan et al, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016 →

A major milestone for the treatment of eye disease De Fauw et al, Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine September 2018 →

Assessing Cardiovascular Risk Factors with Computer Vision. Poplin et al, Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. March 2018 →

Improving the Effectiveness of Diabetic Retinopathy Models: Krause et al, Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. Ophthalmology August 2018 →

Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. Raumviboonsuk et al. NPJ Digital Medicine. April 2019 →

Healthcare AI systems that put people at the center: Beede et al, A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. CHI ’20 April 2020 →

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. MScPH, Yuchen Xie, Quang D. Nguyen BEng, Haslina Hamzah BSc, Gilbert Lim, Valentina Bellemo MSc, Dinesh V. Gunasekeran MBBS, Michelle Y. Yip, et al. The Lancet →

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33 thoughts on “Building AI models for healthcare (ML Tech Talks) technology ai”

  1. the dream was having neural networks decide the trash filter properties, if something is contaminated (cheat) against error. but its also a error if it worked wrong.

  2. What an amazing conversation! Thank you! Interesting point about the dust on the camera, sometimes a simple rubber air dust blower will do 90% of the time before any shots taken (a trick I learnt from photographers). Many clean their lenses with Isopropyl alcohol, and good solvent as it is, will wipe that coating off and damage the image quality later.

  3. That is great !!! Not only in the AI field but Translational field as well. T0:Basic Science -> T1:Translation to Humans -> T2:Translation to Patients -> T3:Translation to Clinical Practice -> T4:Translation to Populations.

  4. It's an interesting topic if you are in healthcare. But, out of curiosity how long does it normally take for an optometrist to view the image and make the diagnoses? I couldn't imagine it being that long as in maybe 10-15 seconds? I suppose this is more useful if you only have a tech on hand and not a optometrist.

  5. ..thank you for a such an informative video very clearly put together, I have been attending IBM EDT courses..specifically the AI practitoner course..the framework addresses similar issues holistically.., 25 years in electronics manufacturing ..I thought I seen it all ..EDT opened my eyes wider and let in more knowledge….I am passionate and know I can contribute in AI healthcare ..but dont know how to get in

  6. Thank you for this amazingly insightful vlog that illustrates the real life challenges of ML and the secret sauce is persistence, skepticism and the ability to work together for a solution. That is why, attitude and culture is critical if we want to create useful models that overcome over innate biases and misconceptions. Grateful for your insights.

  7. Love this video is very informative. Clearly one needs familiarity with healthcare in order to build good use cases since business objectives could be different than other businesses .

  8. 23:04 “Why do they don’t come back after they got diagnosed for follow up?” Simple — You live in a country without general healthcare where treating an illness, getting there, staying healthy is a financial burden. Talking about Thailand and the United State of America. 🤷🏻‍♂️

  9. Great video! I do like to hear more and more about data quality over data quantity, sounds promising. Btw, did you guys where trying to make a reference to Transformers while saying "is all you need" multiple times? 😀

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