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AI and the NHS: How AI will change everything for patients and doctors (AI/Dermatology - 2018-11 - ZDNet)

The UK government’s vision for AI use in the NHS involves transforming the prevention, early diagnosis and treatment of chronic diseases by 2030. AI could become the first point of contact for the sick, could help healthcare professionals to diagnose medical conditions, and monitor individuals’ health by analysing data from their wearable devices or smart-home sensors.

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Healthcare: 5 digital trends for 2019 and beyond (AI/Dermatology - 2018-11 -

Five progressive digital trends for healthcare in 2019 and beyond: 1. Artificial Intelligence (Medical diagnosis, Pharmaceutical product development, Workflow optimization); 2. Big Data & Analytics; 3. The Internet of Medical Things; 4. Telemedicine; 5. VR/AR (Emergency response, Prevention and diagnostics, Surgery, Education, Rehabilitation and emotional recovery).

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Stanford researchers create algorithm to interpret chest x-rays (AI/Dermatology - 2018-11 -

A new AI algorithm can reliably screen chest X-rays for 14 different pathologies: For 10, the algorithm performed just as well as radiologists; for 3, it underperformed them; and for 1, it outdid them. In all cases, the algorithm took <1% time. Show full article

Google AI claims 99% accuracy in metastatic breast cancer detection (AI/Dermatology - 2018-10 - VentureBeat)

Of the 500,000 deaths worldwide caused by breast cancer, an estimated 90% are the result of metastasis. Researchers at the Naval Medical Center San Diego and Google AI have developed algorithms that autonomously evaluate lymph node biopsies, achieving 99% detection accuracy (compared with human pathologists, who may miss small metastases as much as 62% when under time constraints).

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A new wave of chatbots are replacing physicians and providing frontline medical advice (AI/Dermatology - 2018-10 - MIT Technology Review)

AI chatbot apps, such as Babylon Health, a London-based digital-first health-care provider that is working within the National Health Service, are designed to reduce unnecessary visits to general practitioners, while providing immediate medical advice. Babylon’s AI scored 81% on a version of the final exam of the UK Royal College of General Practitioners (UK), 9% higher than the average grade achieved by UK medical students.

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AI algorithm used to adjust treatment dosages for metastatic cancer (AI/Dermatology - 2018-10 -

National University of Singapore researchers used the CURATE.platform to deliver optimal doses of medication and halt the progression of a patient’s advanced prostate cancer.

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Machine learning outperforms clinicians in predicting outcomes for people at risk of psychosis and depression (AI/Dermatology - 2018-09 -

An Australian research study used a cmbination of machine learning algorithms to accurately predict social outcomes one year later in up to 83% of patients at high risk of psychosis and 70% of patients with recent-onset depression.

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New AI system detects hard-to-spot cancerous lesions (AI/Dermatology - 2018-08 -

A team of engineering and medicine researchers at the University of Central Florida has recently developed a new AI system to spot often-missed cancerous tumours on computerised tomography scans. It was 95% successful (compared with 65% for radiologists).

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Big data and Deep Learning used to predict the fate of inpatients (AI/Dermatology - 2018-08 - ZDNet)

Researchers from Google Brain and Stanford University are using big data and deep-learning methods to predict the fate of inpatients, including death; readmissions to measure quality of care; a patient’s length of stay to measure of resource utilization; and a prediction of a patient’s diagnoses to see how well clinicians understood a patient’s problems.

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AI Neural network matches human cardiologists in detecting heart attacks (AI/Dermatology - 2018-07 - MIT Technology Review)

German researchers at the Fraunhofer Heinrich Hertz Institute (Berlin) and the University Medical Center Schleswig-Holstein (Kiel) have developed a neural network that can spot the signs of myocardial infarction, matching the performance of human cardiologists for the first time.

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AI is better than dermatologists at diagnosing skin cancer (AI/Dermatology - 2018-05 - ScienceBlog)

Researchers in Germany, the USA, and France trained a deep learning convolutional neural network to identify skin cancer by showing it more than 100,000 images of malignant melanomas, as well as benign moles. Its performance was better than that of 58 international dermatologists.

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Diagnostic imaging computers outperform human counterparts (AI/Diagnosis - 2018-04 - Case Western Daily)

‘Deep learning’ computers in Case Western Reserve university’s diagnostic imaging lab routinely defeat their human counterparts in detecting various cancers and predicting their strength. Case studies:
• Diagnosing heart failure: 97% accuracy c.f. 74% for two pathologists.
• Distinguishing benign from malignant lung nodules on CAT scans: 5-8% superior to two human experts.
• Prostate cancer scans: computational imaging algorithms detected cancer in an MRI scan in >70% of cases where radiologists missed and correctly detected no cancer in 50% of cases where radiologists reported cancer.

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AI is quicker and more effective than humans in analyzing heart scans (AI/Imaging - 2018-03 -

UC San Francisco research showed that advanced machine learning can classify essential views from heart ultrasound tests faster, more accurately, and with less data than board-certified echocardiographers.180,000 real-world echocardiogram images were used to train a computer to assess the most common echocardiogram views. Both the computer and skilled human technicians were tested on new samples. The computers accurately assessed images 91.7-97.8% of the time, versus 70.2-83.5% for the technicians.

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AI can diagnose prostate cancer as well as a pathologist (AI/Pathology - 2018-03 - Science Business)

Confirmation of a prostate cancer diagnosis normally requires a biopsy sample to be examined by a pathologist. Chinese researchers have developed an AI system with similar levels of accuracy to pathologists, while accurately classifying the level of malignancy of cancer, eliminating the variability which can creep into human diagnoses.

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AI diagnoses eye diseases within 30 seconds with 95% accuracy (AI/Opthamology - 2018-02 -

Researchers at UC San Diego, with colleagues in China, Germany, and Texas, have developed a new computational tool to screen patients with possible macular degeneration and diabetic macular edema. Machine-derived diagnoses were compared with diagnoses from 5 ophthalmologists who reviewed the same scans. With simple training, the machine performed similar to the ophthalmologists, generating a decision on whether or not the patient should be referred for treatment within 30 seconds, with more than 95 percent accuracy.

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AI shown reliable in recognizing and classifying 3 major eye diseases (AI/Opthamology - 2018-01 - Futurism)

A recent study from researchers at the Singapore National Eye Center showed that deep learning software, built to recognize and classify retinal images, was reliable in recognizing diabetic retinopathy, glaucoma, and age-related macular degeneration. This can potentially reduce 80 percent of the workload of graders and optometrists, freeing up their time for treatment.

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A prominent AI researcher suggested that advances in AI mean that medical schools “should stop training radiologists now.” (AI/Radiology - 2018-01 - MIT Technology Review)

Stanford researchers trained a convolutional neural network to detect abnormalities (like fractures, or bone degeneration) better than radiologists in finger and wrist radiographs. (However, radiologists were still better at spotting issues in elbows, forearms, hands, upper arms, and shoulders.) Geoffrey Hinton, a prominent AI researcher, told the New Yorker that advances in AI mean that medical schools “should stop training radiologists now.”

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AI used to treat Bipolar Disorder in an app that could revolutionize medicine (AI/Psychiatry - 2017-06 - ScienceBlog)

David Fleck, an associate professor at the UC College of Medicine, and his co-authors used artificial intelligence called “genetic fuzzy trees” to predict how bipolar patients would respond to lithium. The best of 8 common models used in treating bipolar disorder predicted who would respond to lithium treatment with 75 percent accuracy. By comparison, the AI model was 100% accurate, and even predicted the actual reduction in manic symptoms after lithium treatment with 92% accuracy. Unlike other types of AI, fuzzy logic can describe in simple language why it made its choices. The model could help personalize medicine to individual patients, making health care both safer and more affordable. Fewer side-effects mean fewer hospital visits, less secondary medication, and better treatments.

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Deep-learning Neural Network accurately forecasts onset of Alzheimer’s (AI/Alzheimer's - 2017-04 - MIT Technology Review)

South Korean researchers have developed a deep-learning neural network that can identify, with 81% accuracy, those likely to be diagnosed with Alzheimer’s in the next three years. The evidence continues to suggest that deep-learning machines can spot complex conditions earlier and more accurately than humans.

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Machine Learning algorithm beats ACC-AHA heart attack risk guidelines by 7.6% (AI/Cardiology - 2017-04 - Engadget)

A team of researchers from the UK University of Nottingham has developed a machine-learning algorithm that can predict your likelihood of having a heart attack or stroke better than a doctor, using ACC/AHA guidelines. The neural network algorithm beat the guidelines by 7.6% while raising 1.6% fewer false alarms.

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Google Deep Learning AI diagnoses cancer better than pathologists (AI/Pathology - 2017-03 - Int'l Business Times)

Google has been working on an advanced image-recognition system for several years, initially for the autonomous car project, now for cancer diagnosis. Recently the AI system was pitted against an experienced expert pathologist to examine slides in an unlimited time frame. While the human being achieved 73 percent accuracy, by the end of tweaking, GoogLeNet scored a smooth 89 percent accuracy.

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Smart microscope detects blood infections with 93% accuracy (AI/Microbiology - 2016-12 - FutureScope)

Microbiologists from Harvard’s Beth Israel Deaconess Medical Center have developed a smart microscope that employs AI to accurately diagnose deadly blood infections. The microscope is enhanced with machine learning technology, and initial tests achieved 93% accuracy.

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AI can detect bowel cancer in less than a second with 94% accuracy (AI/Radiology - 2016-10 - ZDNet)

Researchers from Showa University in Yokohama, Japan have built software that can detect bowel cancer in less than a second. In recently-conducted trials, the AI-powered system was able to spot colorectal adenomas — which are benign tumours that can evolve into cancer — from magnified endoscopic images. The images were matched against 30,000 others that were used for machine learning. The system analyzed more than 300 colorectal adenomas in 250 patients, taking less than a second to assess each magnified endoscopic image and determine the malignancy of the tumours with 94 percent accuracy.

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AI reads mammograms with 99% accuracy (AI/Radiology - 2016-09 - Futurism)

A team from the Houston Methodist Research Institute has developed artificial intelligence software that analyzes mammograms for breast cancer with 99% accuracy. This could help keep women from undergoing unnecessary biopsies and would shield them from the agony of false positives.

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