Category: Clinical
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Podcast – Human Compatible: Artificial Intelligence and the Problem of Control
Stuart Russell’s newest work, Human Compatible: Artificial Intelligence and the Problem of Control, is a cornerstone piece, alongside Superintelligence and Life 3.0, that articulates the civilization-scale problem we face of aligning machine intelligence with human goals and values. Not only is this a further articulation and development of the AI alignment problem, but Stuart also…
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Researchers develop an AI system with near-perfect seizure prediction.
…a pair of researchers have created…an AI system that can predict epileptic seizures with 99.6-percent accuracy. Even better, it can do so up to an hour before they occur…giving people enough time to prepare for the attack by taking medication. Hardawar, D. (2019). Researchers develop an AI system with near-perfect seizure prediction. Engagdget. The next…
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AI outperforms clinicians in triaging post-operative patients for ICUe.
Artificial intelligence correctly triaged 41 of the 50 patients in the study (82%). Surgeons had an accuracy triage rate of 70% (35 patients), intensivists 64% (32 patients), and anaesthesiologists 58% (29 patients). The number of incorrect triage decisions was lowest for AI (18%), followed by 30% for surgeons, 36% for intensivists, and 42% for anaesthesiologists.…
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Podcast: What AI means for the physical exam
It’s a very important ritual. If you look at rituals, in general, they are all about crossing a threshold. We marry, we have baptisms, we have funerals—all with ceremony to indicate the crossing of a threshold. If we step back and look at the physical exam, it has all the trappings of ritual. Verghese, A.…
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Podcast series: Medicine and the machine.
A relatively new podcast series, hosted by Medscape, where Eric Topol and Abraham Verghese discuss the implications of artificial intelligence on medicine.
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Survey: Physiotherapy clinicians’ perceptions of artificial intelligence in clinical practice
We know very little about how physiotherapy clinicians think about the impact of AI-based systems on clinical practice, or how these systems will influence human relationships and professional practice. As a result, we cannot prepare for the changes that are coming to clinical practice and physiotherapy education. The aim of this study is to explore…
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Research project exploring clinicians’ perspectives of the introduction of ML into clinical practice
I recently received ethics clearance to begin an explorative study looking at how physiotherapists think about the introduction of machine learning into clinical practice. The study will use an international survey and a series of interviews to gather data on clinicians’ perspectives on questions like the following: What aspects of clinical practice are vulnerable to…
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Article published – An introduction to machine learning for clinicians
It’s a nice coincidence that my article on machine learning for clinicians has been published at around the same time that my poster on a similar topic was presented at WCPT. I’m quite happy with this paper and think it offers a useful overview of the topic of machine learning that is specific to clinical…
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WCPT poster: Introduction to machine learning in healthcare
My poster and list of references for the WCPT 2019 conference in Geneva.
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Giving algorithms a sense of uncertainty could make them more ethical
The algorithm could handle this uncertainty by computing multiple solutions and then giving humans a menu of options with their associated trade-offs. Say the AI system was meant to help make medical decisions. Instead of recommending one treatment over another, it could present three possible options: one for maximizing patient life span, another for minimizing…
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Algorithmic de-skilling of clinical decision-makers
What will we do when we don’t drive most of the time but have a car that hands control to us during an extreme event? Agrawal, A., Gans, J. & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Before I get to the takehome message, I need to set this up a…
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Questions for Artificial Intelligence in Health Care
Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems. This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically,…
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Split learning for health: Distributed deep learning without sharing raw patient data
Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding…
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E.J. Chichilnisky | Restoring Sight to the Blind
Source: After on podcast with Rob Reid: Episode 39: E.J. Chichilnisky | Restoring Sight to the Blind. This was mind-blowing. The conversation starts with a basic overview of how the eye works, which is fascinating in itself, but then they start talking about how they’ve figured out how to insert an external (digital) process into…
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The fate of medicine in the time of AI
Source: Coiera, E. (2018). The fate of medicine in the time of AI. The challenges of real-world implementation alone mean that we probably will see little change to clinical practice from AI in the next 5 years. We should certainly see changes in 10 years, and there is a real prospect of massive change in…
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Medical data: who owns it and what can be done to it?
…most states in the US do not have law to confer specific ownership of medical data to patients, while others put the rights on hospitals and physicians. Of all, only New Hampshire allows patients to legally own their medical records. Source: Medical data: who owns it and what can be done to it? A short…
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adapting to constant change
The human work of tomorrow will not be based on competencies best-suited for machines, because creative work that is continuously changing cannot be replicated by machines or code. While machine learning may be powerful, connected human learning is novel, innovative, and inspired. Source: Jarche, H. (2018). adapting to constant change. A good post on why…
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Paper Review: the Babylon Chatbot
…it is fantastic that Babylon has undertaken this evaluation, and has sought to present it in public via this conference paper. They are to be applauded for that. One of the benefits of going public is that we can now provide feedback on the study’s strength and weaknesses. Source: Coiera, E. (2018). Paper Review: the…
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If Artificial Intelligence Only Benefits a Select Few, Everyone Loses
…nations that have begun to prepare for and explore AI will reap the benefits of an economic boom. The report also demonstrates how anyone who hasn’t prepared, especially in developing nations, will be left behind… In the developing world, in the developing countries or countries with transition economies, there is much less discussion of AI,…
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Doctors are burning out because electronic medical records are broken
For all the promise that digital records hold for making the system more efficient—and the very real benefit these records have already brought in areas like preventing medication errors—EMRs aren’t working on the whole. They’re time consuming, prioritize billing codes over patient care, and too often force physicians to focus on digital recordkeeping rather than…