Category: Clinical
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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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The first AI approved to diagnose disease is tackling blindness in rural areas
There are any number of reasons why people don’t get medical care or don’t follow up on a referral to a specialist. They might not think they have a serious problem. They might lack time off work, reliable transportation, or health insurance. And those are problems AI alone can’t solve. Source: Mullin, E. (2018). The…
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The Desperate Quest for Genomic Compression Algorithms
While it’s hard to anticipate all the future benefits of genomic data, we can already see one unavoidable challenge: the nearly inconceivable amount of digital storage involved. At present the cost of storing genomic data is still just a small part of a lab’s overall budget. But that cost is growing dramatically, far outpacing the…
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Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
Based on these results, FDA authorized the system for use by health care providers to detect more than mild DR and diabetic macular edema, making it, the first FDA authorized autonomous AI diagnostic system in any field of medicine, with the potential to help prevent vision loss in thousands of people with diabetes annually. Source:…
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Facebook and NYU Using AI to Speed Up MRIs
The Facebook/NYU partnership is working to minimize the amount of data that is captured, instead relying on computers to reconstruct the image from imperfect inputs. If this is successful, we may see a 10x reduction in scan times, which would lead to lower costs for MRIs and a much greater utilization of these machines Source:…
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How to ensure safety for medical artificial intelligence
When we think of AI, we are naturally drawn to its power to transform diagnosis and treatment planning and weigh up its potential by comparing AI capabilities to those of humans. We have yet, however, to look at AI seriously through the lens of patient safety. What new risks do these technologies bring to patients,…
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3 Ways AI Is Getting More Emotional
AI systems and devices will soon recognize, interpret, process, and simulate human emotions. A combination of facial analysis, voice pattern analysis, and deep learning can already decode human emotions for market research and political polling purposes. Source: Kleber, S. (2018). 3 Ways AI Is Getting More Emotional. There are currently 3 categories for what is…
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I didn’t become a physician to do data entry
I opened Ms. Tucker’s chart. There were twenty-one tabs vertically on the left-hand corner of the screen and eighteen tabs horizontally on the top of the screen. I quickly glanced through the cluttered twenty-one vertical tabs; I clicked on the one I am looking for — “transfer medication reconciliation” in the 19th slot. A new…
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Translating AI into the clinical setting at UC Irvine – AI Med
Ultimately, many of these shortcomings exist because few if any physicians are actively engaged in developing the next generation of technology, AI or otherwise. It is interesting to note the vast majority of medical startup companies are founded with limited if any physician involvement or oversight.Without experts that deeply understand both the medical and technical…
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Dina Katabi: A new way to monitor vital signs (that can see through walls) | TED Talk
So if you think about it, wireless signals, they travel through space, they go through obstacles and walls and occlusions, and some of them, they reflect off our bodies, because our bodies are full of water, and some of these minute reflections, they come back. And if, just if, I had a device that can…
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DeepMind’s AI can detect over 50 eye diseases as accurately as a doctor.
This is the point at which the risk from medical AI becomes much greater. Our inability to explain exactly how AI systems reach certain decisions is well-documented. And, as we’ve seen with self-driving car crashes, when humans take our hands off the wheel, there’s always a chance that a computer will make a fatal error…
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Emotions and assessment: considerations for rater‐based judgements of entrustment
We identify and discuss three different interpretations of the influence of raters’ emotions during assessments: (i) emotions lead to biased decision making; (ii) emotions contribute random noise to assessment, and (iii) emotions constitute legitimate sources of information that contribute to assessment decisions. We discuss these three interpretations in terms of areas for future research and…
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The Future of Artificial Intelligence Depends on Trust
To open up the AI black box and facilitate trust, companies must develop AI systems that perform reliably — that is, make correct decisions — time after time. The machine-learning models on which the systems are based must also be transparent, explainable, and able to achieve repeatable results. Source: Rao, A. & Cameron, E. (2018).…
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MIT Creates AI to Optimize Brain Cancer Treatment
The goal [with chemotherapy] is basically to poison the tumor cells faster than non-cancerous cells, but the side effects of going after an aggressive disease like this can be devastating. These traditional treatment schedules don’t take into account differences in tumor size, medical histories, genetic profiles, and biomarkers. The system developed by MIT does that,…
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Lip-reading artificial intelligence could help the deaf—or spies | Science | AAAS
The researchers started with 140,000 hours of YouTube videos of people talking in diverse situations. Then, they designed a program that created clips a few seconds long with the mouth movement for each phoneme, or word sound, annotated. The program filtered out non-English speech, nonspeaking faces, low-quality video, and video that wasn’t shot straight ahead.…
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AI at Google: Our principles
Be socially beneficial Avoid creating or reinforcing unfair bias Be built and tested for safety Be accountable to people Incorporate privacy design principles Uphold high standards of scientific excellence Be made available for uses that accord with these principles Source: AI at Google: Our principles This list isn’t a bad start if you’re looking for…
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Defensive Diagnostics: the legal implications of AI in radiology
Doctors are human. And humans make mistakes. And while scientific advancements have dramatically improved our ability to detect and treat illness, they have also engendered a perception of precision, exactness and infallibility. When patient expectations collide with human error, malpractice lawsuits are born. And it’s a very expensive problem. Source: Defensive Diagnostics: the legal implications…
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a16z Podcast: Putting AI in Medicine, in Practice
A wide-ranging conversation on several different aspects of AI in medicine. Some of the key takeaways for me included: AI (in it’s current form) has some potential for long-term prediction (e.g. you have an 80% chance of developing diabetes in the next 10 years) but we’re still very far from accurate short-term prediction (e.g. you’re…
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IBM’s Watson gave unsafe recommendations for treating cancer
In 2012, doctors at Memorial Sloan Kettering Cancer Center partnered with IBM to train Watson to diagnose and treat patients. But according to IBM documents dated from last summer, the supercomputer has frequently given bad advice, like when it suggested a cancer patient with severe bleeding be given a drug that could cause the bleeding…
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Eagle-eyed machine learning algorithm outdoes human experts — ScienceDaily
“Human detection and identification is error-prone, inconsistent and inefficient. Perhaps most importantly, it’s not scalable,” says Morgan. “Newer imaging technologies are outstripping human capabilities to analyze the data we can produce.” Source: Eagle-eyed machine learning algorithm outdoes human experts — ScienceDaily The point here is that data is being generated faster than we can analyse…