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, […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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, […]

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 […]

Ontario is trying a wild experiment: Opening access to its residents’ health data

This has led companies interested in applying AI to healthcare to find different ways to scoop up as much data as possible. Google partnered with Stanford and Chicago university hospitals to collect 46 billion data points on patient visits. Verily, also owned by Google’s parent company Alphabet, is recruiting 10,000 people for its own long-term […]

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