IBM Watson Health plans to make a 10-year, $50 million investment in research collaborations.
This was revealed at the IBM Think 2019 event in San Francisco this week.
The computing company is collaborating with two separate academic centres: Brigham and Women's Hospital, which is a teaching hospital of Harvard Medical School, and Vanderbilt University Medical Centre, to advance the science of artificial intelligence (AI) and its application to major public health issues.
IBM's supercomputer Watson was created as a question answering computing system that the company built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.
In recent years, the Watson capabilities have been extended and the way in which Watson works has been changed to take advantage of new deployment models and evolved machine learning capabilities.
In healthcare, Watson's natural language, hypothesis generation and evidence-based learning capabilities are being investigated to see how Watson may contribute to clinical decision support systems and the increase in AI in healthcare for use by medical professionals.
Electronic health records
In a statement, IBM says the scientific collaborations with each institution will focus on critical health problems that are ideally suited for AI solutions.
Initial areas of study are expected to include the use of AI to improve the utility of electronic health records and claims data to address significant public health issues like patient safety, precision medicine and health equity. The research will also explore physician and patient user experience and interactions with AI technologies.
"Building on the MIT-IBM Watson Lab announced last year, this collaboration will include contributions from IBM Watson Health's longstanding commitment to scientific research and our belief that working together with the world's leading institutions is the fastest path to develop, advance and understand practical solutions that solve some of the world's biggest health challenges," says Kyu Rhee, vice-president and chief health officer at IBM Watson Health.
"Today, for example, physicians are spending an average of two hours with their electronic health records and deskwork for every hour of patient care, a phenomenon the American Medical Association says is leading to a steady increase in physician burnout.
"AI is the most powerful technology we have today to tackle issues like this one, but there is still a great deal of work to be done to demystify the real role of AI in healthcare with practical, proven results and clear-cut best practices. By putting the full force of our clinical and research team together with two of the world's leading academic medical centres, we will dramatically accelerate the development of real-world AI solutions that improve workflow efficiencies and outcomes," says Rhee.
Cardiovascular diseases
Meanwhile, IBM Watson Health and the Broad Institute of MIT and Harvard are expanding their partnership to help clinicians better predict the possibility of serious cardiovascular diseases.
By working with genomics, clinical data and AI, IBM and the Broad Institute hope this three-year project will help provide doctors with tools to tap into the potential of genomics data, and better understand the intrinsic possibility an individual has for a certain disease.
Equipped with this knowledge, health professionals can potentially intervene and help to reduce this risk, says IBM.
It notes this initiative will incorporate population-based and hospital-based biobank data, genomic information, and electronic health records to build upon and expand the predictive power of polygenic scoring, otherwise known as genetic risk scoring.
IBM and the Broad Institute aim to build algorithms that can pinpoint and learn from trends in these data points, and then indicate a potential predisposition to certain health conditions.
The project will also plan to make insights and tools widely available to the research community, including methods to calculate an individual's risk of developing common diseases based on millions of variants in the genome.
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