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The use of AI technologies has been explored for use in the diagnosis and prognosis of Alzheimer's disease (AD). For diagnostic purposes, machine learning models have been developed that rely on structural MRI inputs. [75] The input datasets for these models are drawn from databases such as the Alzheimer's Disease Neuroimaging Initiative. [76]
A 2019 study found that an algorithm used to predict health care needs for more than 100 million people was biased against Black patients. “The algorithm relied on health care spending to ...
The scale and capabilities of artificial intelligence (AI) systems are growing rapidly, notably due to advances in big data. In healthcare, it is expected to provide easier accessibility of information, and to improve treatments while reducing cost. The integration of AI in healthcare tends to improve the quality and efficiency of complex tasks.
Healthcare information technology can also result in iatrogenesis if design and engineering are substandard, as illustrated in a 14-part detailed analysis done at the University of Sydney. [40] Numerous examples of bias introduced by artificial intelligence (AI) have been cited as the use of AI-assisted healthcare increases. See Algorithmic bias.
From AI-powered safety features in cars to cartoon-style robots comforting children at doctor visits, the video painted a picture of AI agents becoming integrated into health care, mobility, and ...
[3] In the late 1990s and early 2000s, AI technology became widely used as elements of larger systems, [3] [4] but the field was rarely credited for these successes at the time. Kaplan and Haenlein structure artificial intelligence along three evolutionary stages: Artificial narrow intelligence – AI capable only of specific tasks;