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How Hit, Including Artificial Intelligence, Supercomputing, and Clinical Support Systems Are Likely to Change Healthcare

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Among the biggest contributors to the high cost of healthcare in the U.S. are the inherent complexities and misaligned financial interests of payers, providers, and patients

Change Healthcare is using Artificial Intelligence (AI) and machine learning (ML) to identify inefficiencies and drive them out of administrative processes in the healthcare system and, as a result, help reduce costs and improve outcomes for payers, providers, and patients.

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The milestones covered by Artificial Intelligence, or AI as it is fondly known, has brought the world on its toes. The level of acceleration of growth in various industries has been pretty quick and sometimes, totally unpredictable

So, what is AI? AI is a collection of multiple technologies that mimic human’s cognitive functions. Apart from all the industries it has been touching, you can say that the modern healthcare industry has been receiving paramount importance. There has been a paradigm shift in the way patients are treated by doctors because they now have inordinate amounts of data in their hands, and a good amount of this data can be put to good use. It is possible to apply AI to both structured and unstructured data, with techniques including machine learning and natural language processing. The technology is widely used in all kinds of health related aspects, but it is also important to note that the largest concentrated usage is cardiology, neurology and oncology.

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Experts predict AI to have a significant impact in diverse areas of health care such as chronic disease management and clinical decision making (Bresnick, 2016). While still in the early stages of adoption, AI algorithms are showing promise in specializations such radiology, pathology, ophthalmology, and cardiology (Hsieh, 2017a). This progress raises a thought-provoking question

Will AI at some point displace certain physicians such as radiologists or will it help make them more effective or will it be a bit of both? This research looks at the potential uses of AI in medicine and considers the possibility of AI replacing certain physicians or at least supplementing the role of physicians. The rest of this paper is organized as follows. A survey of the literature is provided in ‘Literature Survey’. ‘Artificial Intelligence vs. Machine Learning vs. Deep Learning’ provides a discussion on AI, Machine Leaning, and Deep Learning and how they relate to each other. ‘

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To conclude, these challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. As a result, we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10

It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. Over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion and big-picture integration. Perhaps the only healthcare providers who will lose their jobs over time maybe those who refuse to work alongside artificial intelligence.

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Bresnick (2018b) Bresnick J. Top 12 ways artificial intelligence will impact healthcare. 2018b.

Cavallo (2017) Cavallo J. How Watson for oncology is advancing personalized patient care. 2017.

Houssami et al. (2017) Houssami N, Lee CI, Buist D, Tao D. Artificial intelligence for breast cancer screening: Opportunity or hype? The Breast. 2017;36(2017):31–33. doi: 10.1016/

Hsieh (2017a) Hsieh P. AI in medicine: rise of the machines. 2017a.Crevier (1993) Crevier D. AI: the tumultuous search for artificial intelligence. BasicBooks; New York: 1993.

IBM (2015) IBM Manipal hospitals adopts watson for oncology to help physicians identify options for individualized, evidence-based cancer care across India. Press release. 2015.

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