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Country: United States
Metropolitan Area: San Francisco
Postal Reference Code: 94110
Latitude: 37.7506
Longitude: -122.4121




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for the first time ever ai systems can directly harm patients. are we doing enough to prevent a medical ai tragedy, the equivalent of a. explain yourself, machine. producing simple text descriptions for ai interpretability. humans explain their decisions with words. in our latest work, we suggest ai systems should do the same.. the unreasonable usefulness of de*p learning in medical image datasets. medical data is horrible to work with, but de*p learning can quickly and efficiently solve many of these problems.. post-doc and phd positions for de*p and reinforcement learning in medical images. our team has post-doc and phd positions available, so come to unexpectedly great adelaide! the first doctors to feel the effects of automation might not be radiologists at all. it might be surgeons. since the chexnet paper came out in november 2017 i have been communicating with the author team. i'm finally ready to review the i just wanted to do a quick follow up to my recent blog post, which discussed the performance metrics i think might be appropriate for use in medical ai studies. one thing i didn't cover was the reason we might want to use multiple metrics, or the philosophy behind choosing the ones i did. so today, … continue reading the philosophical argument for using roc curves 2017 was cool. medical ai progressed apace, the ai community grew up some and got a bit creative, and i made some predictions that mostly a couple of weeks ago, i mentioned i had some concerns about the chestxray14 dataset. i said i would come back when i had more info, and since then i have been digging into the data. i've talked with dr summers via email a few times as well. unfortunately, this exploration i have been doing a fair bit of thinking about the blog, and wanted to clue you all in on some changes. i had intended to finish my the end of human doctors series before moving on to anything else, and i even let some really cool topics pas* me by to maintain continuity. it …


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Title: Luke Oakden-Rayner – PhD Candidate / Could be improved
Description: PhD Candidate / Could be improved
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/about-me/:
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About me – Luke Oakden-Rayner

Description

I am a radiologist (medical specialist) in South Australia, undertaking a Ph.D in Medicine with the School of Public Health at the University of Adelaide. My research explores the intersection of medical imaging, computer science and future technologies, with specific interests in the application of machine learning to medical images and text, exploitation of our vast but unstructured…

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About me

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/research/:
Title

Research – Luke Oakden-Rayner

Description

Publications, grants and ongoing projects

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Research

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/education/:
Title

Education – Luke Oakden-Rayner

Description

Various presentations/lectures/tutorials are linked below. These are intended to stand-alone to some extent, although they do work much better in person. Each clinical teaching presentation is usually followed by a set of cases, but these are not uploaded at this stage. All presentations should be ed rather than viewed online, as SlideShare does not cope…

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Education

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Med 4 Uni Adelaide

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Lectures

/my-moocs-with-reviews/:
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My MOOCs (with reviews) – Luke Oakden-Rayner

Description

M ive open online courses (MOOCs) have brought the very best teachers of the world to anyone with an internet connection. Over the last two years, I have explored many of these cl es and fully completed several. Computer science / data science : CS231n – Convolutional Neural Networks for Visual Recognition. Audited via Stanford University. This course…

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My MOOCs (with reviews)

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/2018/07/11/medical-ai-safety-we-have-a-problem/:
Title

Medical AI Safety: We have a problem. – Luke Oakden-Rayner

Description

For the first time ever AI systems can directly harm patients. Are we doing enough to prevent a medical AI tragedy, the equivalent of a thalidomide event?

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Medical AI Safety: We have a problem.

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Medical safety

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