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Using AI to infer patterns in disease

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David Tseng, M.D., looks at the future of artificial intelligence in medicine in a Med Page Today piece.

Among his observations:

“Some data are starting to emerge that healthcare metrics can be improved with AI, as evidenced by Emergency Department visits being deceased by AI-based mobile technology in the U.K.”

“{But} the recent dissolution of a partnership between Watson Health and MD Anderson is a reminder that translating these innovations into clinical practice is fraught with challenges. Michael Forsting’s article ‘Machine Learning Will Change Medicine‘┬áin the Journal of Nuclear Medicine grappled with some of these issues.

“One of the potential benefits when integrating AI into medical practice is an improvement of clinical decision-making and diagnosis. Frosting points out that rare diseases are often overlooked and biases can skew diagnostic pathways. The concept of using AI to provide clinical decision support systems for physicians has been studied in some medical specialties with varying degrees of effectiveness. The goal is to provide automation of certain physician cognitive tasks during a medical workup to improve time and accuracy to diagnosis.”

“There is potential for AI to do more than just automate the processes for the diagnosis and treatment of disease. The true power comes from how computing could potentially transcend conventional ways of thinking about disease and medicine. AI can integrate and infer from a much larger dataset than any human can, discerning patterns that are difficult to appreciate from a human perspective.”

To read the full article, please hit this link.



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