IBM’s latest A.I. can help doctors detect how quickly a disease is progressing

While there obviously shouldn’t be an outcry for robots to take the jobs of human physicians, that doesn’t mean artificial intelligence can’t play an important role when it comes to helping doctors carry out certain aspects of their jobs — such as recognizing early symptoms of serious diseases that can go unnoticed. A delay in diagnosis can make it more difficult to implement effective treatments or even find cures.

This is something that IBM wants to help with, courtesy of a new probabilistic A.I. model that can be used to better understand patients’ individual conditions and, crucially, how they are likely to develop. It can give information about a wide range of symptoms — from shaking hands to mood swings — and then use this data to help identify the biomarkers of diseases including diabetes, Huntington’s disease, Alzheimer’s, Parkinson’s, and more. The A.I. can help doctors understand more about the progression of these neurological disorders, as well as pinpoint how advanced they are.

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“What makes this model unique is that it analyzes multiple clinical aspects and symptoms of a neurodegenerative disease at the same time,” Soumya Ghosh, a research staff member in the Health Analytics Research Group at IBM Research, told Digital Trends. “Taking into account all of the effects of a disease — such as psychological, behavioral, cognitive and physical symptoms — can give a much more accurate analysis of where a patient may be in the progression of their condition, such as Huntington’s disease.”




The data fed into IBM’s probabilistic models could be either self-reported or otherwise gathered by a doctor. It’s also quite possible that this could somehow link in with IBM’s other recent work in the clinical care field, such as its fingernail-mounted wearable that’s designed to measure grip strength throughout the day. Other related projects being carried out by IBM include A.I. research to identify how diseases such as Huntington’s affect the molecular structure of the brain, as well a collaboration with the Michael J. Fox Foundation for Parkinson’s Research to identify possible indicators of Parkinson’s disease.

“We hope that one day, using machine learning and A.I. to track conditions in this way will allow for clinicians to make more accurate diagnoses, design more effective treatments, and give patients more information into the stage of their disease and how they can manage it,” Ghosh said. “Our work is purely research at this point in time, and we currently do not have a set timeline for building this into a commercial product. However, we look forward to continuing to build on this work, and exploring how this research can take root and evolve.”

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This article was originally published by Digital Trends. Read the original article here.

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