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Startup lets doctors classify skin conditions with the snap of a picture

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At the age of 22, when Susan Conover wanted to get a strange-looking mole checked out, she was told it would take three months to see a dermatologist. When the mole was finally removed and biopsied, doctors determined it was cancerous. At the time, no one could be sure the cancer hadn't spread to other parts of her body -- the difference between stage 2 and stage 3 or 4 melanoma. Thankfully, the mole ended up being confined to one spot. But the experience launched Conover into the world of skin diseases and dermatology.


Can AI Help Fight Cancer?

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The short answer is yes – cancer and other health problems too. Artificial Intelligence (AI) is a game-changer. Not only can this rapidly advancing technology improve the speed and accuracy of disease diagnosis and treatment, it has enormous potential to predict health problems, allowing for far more effective prevention programs that target at-risk populations. Take, for example, children born with congenital heart defects. This fate currently falls to about 40,000 babies born in the U.S. each year, and about 1.35 million newborns worldwide.


Drowning in Data

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In 1945 the volume of human knowledge doubled every 25 years. Now, that number is 12 hours [1]. With our collective computational power rapidly increasing, vast amounts of data and our ability to assimilate it, has seeded unprecedented fertile ground for innovation. Healthtech companies are rapidly sprouting from data ridden soil at exponential rates. Cell free DNA companies, once a rarity, are becoming ubiquitous. The genomics landscape, once dominated by the few, are being inundated by a slew of competitors. Grandiose claims of being able to diagnose 50 different cancers from a single blood sample, or use AI to best dermatologists, radiologists, pathologists, etc., are being made at alarming rates. Accordingly, it's imperative to know how to assess these claims as fact or fiction, particularly when such claimants may employ "statistical misdirection". In this addition to "The Insider's Guide to Translational Medicine" we disarm perpetrators of statistical warfare of their greatest ...


The Doctor Game: Can artificial intelligence help fight cancer?

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Artificial intelligence (A.I.) is a game-changer. Not only can this rapidly advancing technology improve the speed and accuracy of disease diagnosis and treatment, it has enormous potential to predict health problems, allowing for far more effective prevention programs that target at-risk populations. Take, for example, children born with congenital heart defects. This fate currently falls to about 40,000 babies born in the U.S. each year, and about 1.35 million newborns worldwide. What causes defective heart structures in the developing embryo is open to debate.


Does This Artificial Intelligence Think Like A Human? - Liwaiwai

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In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo. While tools exist to help experts make sense of a model's reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns. Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model's behavior.


Does this artificial intelligence think like a human?

#artificialintelligence

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo. While tools exist to help experts make sense of a model's reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns. Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model's behavior.


Using artificial intelligence to diagnose cancer

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During her Ph.D., Dr. Qurrat Ul Ain developed a computer-aided diagnostic system that can identify certain characteristics of the disease from a photograph of a skin lesion. "Skin cancer has certain unique visual features that help to differentiate it from normal skin," Dr. Qurrat Ul Ain says. "These include color, texture, and the shape of lesions. By showing our artificial intelligence program images of cancerous skin, we were able to teach it to identify cancer when shown other photographs." Dr. Qurrat Ul Ain's diagnostic system achieved a 100% accuracy rating in identifying images of melanoma based on the more than 600 images tested so far.


AI May Be Catching up With Human Reasoning

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A new technique that measures the reasoning power of artificial intelligence (AI) shows that machines are catching up to humans in their abilities to think, experts say. Researchers at MIT and IBM Research have created a method that enables a user to rank the results of a machine-learning model's behavior. Their technique, called Shared Interest, incorporates metrics that compare how well a model's thinking matches people's. "Today, AI is capable of reaching (and, in some cases, exceeding) human performance in specific tasks, including image recognition and language understanding," Pieter Buteneers, director of engineering in machine learning and AI at the communications company Sinch, told Lifewire in an email interview. "With natural language processing (NLP), AI systems can interpret, write and speak languages as well as humans, and the AI can even adjust its dialect and tone to align with its human peers."


Using artificial intelligence to diagnose cancer

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Te Herenga Waka-Victoria University of Wellington PhD graduate Dr Qurrat Ul Ain has developed an artificial intelligence programme that could help diagnose skin cancer, using just a photograph. During her PhD, Dr Qurrat Ul Ain developed a computer-aided diagnostic system that can identify certain characteristics of the disease from a photograph of a skin lesion. "Skin cancer has certain unique visual features that help to differentiate it from normal skin," Dr Qurrat Ul Ain says. "These include colour, texture, and the shape of lesions. By showing our artificial intelligence programme images of cancerous skin, we were able to teach it to identify cancer when shown other photographs."


Do Humans and AI Think Alike?

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MIT researchers developed a method that helps a user understand a machine-learning model's reasoning, and how that reasoning compares to that of a human. A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model's behavior. In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo. While tools exist to help experts make sense of a model's reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated.