Deep Learning Applications in Medicine & Insurancec847c82b-4779-4e20...IMS 2017 • Deep Learning is...

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Deep Learning Applications in Medicine & Insurance Insurance Medicine Summit 2017 Daniel Thyssen, L&H IT Business Partner EMEA, Swiss Re Urs Widmer, Senior Medical Officer, Swiss Re

Transcript of Deep Learning Applications in Medicine & Insurancec847c82b-4779-4e20...IMS 2017 • Deep Learning is...

Page 1: Deep Learning Applications in Medicine & Insurancec847c82b-4779-4e20...IMS 2017 • Deep Learning is a specialised field in Machine Learning that uses neural networks which are trained

Deep Learning Applications in Medicine & Insurance

Insurance Medicine Summit 2017

Daniel Thyssen, L&H IT Business Partner EMEA, Swiss ReUrs Widmer, Senior Medical Officer, Swiss Re

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IMS 2017

Machine Intelligence is a very broad discipline, including various computational/modelling methods and applying to a large variety of data

Machine Intelligence overview

MachineIntelligence

Slide: Gianluca Antonini

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IMS 2017

Machine Intelligence refers to the interplay between Artificial Intelligence, Machine Learning and Cognitive Computing

Machine Intelligence overview

Slide: Gianluca Antonini

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IMS 2017

• BBC Series on “The Mind Machine”

• A computer-based neural network was used to determine whether London Underground platforms were empty, half-full or full

• Result: fed with 1000s of labelled CCTV pictures, the model learned to pick out the best predictive features – the part of the pictures where (dark) passenger heads would obscure the (light) platform floor

This is all-new, exciting stuff. Isn’t it ?Exciting, yes.

All new ? Look back to the early 1990s…

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successful application, however substantial training involved for a relatively simple task

© BBC

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IBM Watson for Oncology

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https://www.ibm.com/watson/health/oncology-and-genomics/oncology/https://youtu.be/8_bi-S0XNPI

• very specific to oncology

• very high expert effort to train the neural network

https://www.ibm.com/watson/health/oncology-and-genomics/oncology/
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https://www.theguardian.com/technology/2017/nov/03/googles-ai-turtle-rifle-mit-research-artificial-intelligence

can be prone to “obvious mistakes”

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• Deep Learning is a specialised field in Machine Learning that uses neural networks which are trained on large volumes of expert-labelled sample data

• Deep Learning can be very powerful in well-defined, well-trained areas (such as oncology)

• Training effort and cost are typically very high, and results can’t be transferred to other content areas

• While general accuracy in a well-trained model can be very high, unexpected “obvious” mistakes can occur

• Legal challenges arise where Deep Learning is to be used for decision-making, as there are no transparent causal decision paths. The usual way out of this dilemma is to use Deep Learning for decision support.

Deep Learning - Highlights

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powerful but very specific – and expensive

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Lakshadweep IslandsSouthern India

Binary judgment benign OR malignant?reassure OR biopsy?

“…almost anything that a typical human can do with less than 1 second of mental thought, we can probably automate using AI.” (Andrew Ng, Interview WSJ)

Agenda1. Skin Cancer2. A.I. Heroes3. Diabetic Retinopathy4. The future

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Artificial Intelligence

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IBM Watson

Google DeepMind; Deep Learning

Slumdog Millionaire

JAMA. 2016 Dec 13;316(22):2402-2410

Nature. 2017 Feb 2;542(7639):115-118

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Digital doctor: AI singles out skin cancer from photos

https://www.youtube.com/watch?v=IvmLEq9piJ4&t=6s

https://www.youtube.com/watch?v=IvmLEq9piJ4&t=6s
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Lesions learnt…

https://www.tensorflow.org/

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Performance of the CNN vs dermatologists

CNN vs. 21 dermatologists at keratinocyte ca and melanoma recognition. Biopsy-proven images of lesions are

displayed, and dermatologists are asked if they would: biopsy/treat the lesion OR reassure the patient?

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The rise of AIFrom Theseus to Cubli1952 - 2017

1952 “Theseus” Maze-Solving Mouse and Claude Shannon

Raffaello D’AndreaETH Institute for Dynamic Systems and Control

A 15 x 15 x 15 cm cube that can jump up and balance on a corner, using reaction wheels mounted on three of its faces

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IMS 2017 14https://www.economist.com/news/americas/21730905-country-has-made-virtue-out-limited-resources-how-canadas-unique-research-culture-has

1982 Fraser Mustard, a doctor, founded the Canadian Institute for Advanced Research

Maple Valley

Geoffrey Hinton joined CIFAR to set up a group dedicated to neural networks which included Sebastian Thrun, Terry Sejnowski , Jeff Hawkins, Ruslan Salakhutdinov.

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Case Study: TensorFlow in Medicine - Retinal Imaging (TensorFlow Dev Summit 2017)

https://www.youtube.com/watch?v=oOeZ7IgEN4o

https://www.youtube.com/watch?v=oOeZ7IgEN4o
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Personalized Nutrition by Prediction of Glycemic Responses

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What is the best diet for humans? | Eran Segal | TEDxRuppin

https://www.youtube.com/watch?v=0z03xkwFbw4

Cell. 2015 Nov 19;163(5):1079-94. doi: 10.1016/j.cell.2015.11.001.

https://www.youtube.com/watch?v=0z03xkwFbw4
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What the future bringsA.I. - A job displacement issue in Medicine?

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http://www.rnrglatt.ch/ Mathematicians Land Top Spot in New Ranking of Best and Worst Occupations in the U.S

https://www.wsj.com/articles/SB123119236117055127

AI is the new electricity

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IMS 2017 18https://www.coursera.org/specializations/deep-learning

Coursera founders Andrew Ng and Daphne Koller are computer science professors at Stanford University

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IMS 2017 19Unesco World Heritage Site: Swiss Tectonic Arena Sardona

[email protected] [email protected]

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Legal notice

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©<year> Swiss Re. All rights reserved. You are not permitted to create any modifications or derivative works of this presentation or to use it for commercial or other public purposes without the prior written permission of Swiss Re.

The information and opinions contained in the presentation are provided as at the date of the presentation and are subject to change without notice. Although the information used was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the details given. All liability for the accuracy and completeness thereof or for any damage or loss resulting from the use of the information contained in this presentation is expressly excluded. Under no circumstances shall Swiss Re or its Group companies be liable for any financial or consequential loss relating to this presentation.


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