Building AI models for healthcare (ML Tech Talks)
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 → https://goo.gle/3gVhTxs
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 → https://goo.gle/35Sfs9C
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 → https://goo.gle/3qkg01I
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 → https://goo.gle/3gR8d8n
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. Raumviboonsuk et al. NPJ Digital Medicine. April 2019 → https://goo.gle/2SmyXUO
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 → https://goo.gle/3ja6TyP
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 → https://goo.gle/3zVec3q
Catch more ML Tech Talks → http://goo.gle/ml-tech-talks
Subscribe to TensorFlow → https://goo.gle/TensorFlow
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 → https://goo.gle/3gVhTxs
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 → https://goo.gle/35Sfs9C
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 → https://goo.gle/3qkg01I
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 → https://goo.gle/3gR8d8n
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. Raumviboonsuk et al. NPJ Digital Medicine. April 2019 → https://goo.gle/2SmyXUO
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 → https://goo.gle/3ja6TyP
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 → https://goo.gle/3zVec3q
Catch more ML Tech Talks → http://goo.gle/ml-tech-talks
Subscribe to TensorFlow → https://goo.gle/TensorFlow
