In this session of Machine Learning Tech Talks, Product Manager Lily Peng will discuss the three common myths in building AI models for healthcare.
Chapters:
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
Resources:
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 comments
46:20 general rule to build better models: be skeptical. Question easy good results (a bug is there probably) since good model are very unlikely to be built easily.
42:10 Lesson 3: solve a problem with genuinely clinical utility. Example at 43::00
38:00 Lesson 2: question construction of data sets. Interact with data curator to avoid model cheating. Example of tuberculosis identification from chest images.
35:10 great 3 lessons for building healthcare models, from an expert:
1. Look at the data. Browse them even not an expert. At 36:30 Funny story of the model detecting circled notes on images due to data contamination.
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.
This was superb, thanks for all the insights and pro tips
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.
I'm gonna hit the like button 10 times. Machine learning is the best.
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.
This is so inspiring . I am a medical student too and studying ai on my own . Thankful for coming across this video
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.
IN Health Care in order to build a "good" dataset it's needed a sound background in epidemiology and in medical statistics.
Great explanation really easy to understand!
..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
Great lessons that we can't find in school.
Why I think I've seen this conversation like 3 or 4 years ago somewhere on the internent, am I hallucinating or what..
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.
It seems that most of the problems mentioned regarding the design of the Product are related to not having a standarized Design for Machine Learning best practice.
Thanks. Its very resourceful. I am curios if pre-trained model is available.
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 .
A great talk. I appreciate all the sharing in this video <3
Thank you!
THIS IS cOOL IT wILL REALLY HELP
Great video & conversation. Watched from beginning to end, couldn't stop. Looking forward to more content like this.
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.
More videos about the applications of AI in Healthcare please
Simple but very informative to drive AI toward useful productivity.
Great video, thanks!
Are there any services where you can find highly qualified, for example, doctors, for data labelling?
It would be great to have more videos about the applications of AI in Healthcare
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?
What an amazing conversation. Thank you for sharing!
class!!
Simply wonderful. Thank you!