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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 ...
AI in healthcare is often used for classification, to evaluate a CT scan or electrocardiogram or to identify high-risk patients for population health. AI is helping with the high-cost problem of dosing. One study suggested that AI could save $16 billion.
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.
It's being used in many industries worldwide. Whisper was created by OpenAI. Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said
AI in mental health is still an emerging field and there are still some concerns and criticisms about the use of AI in this area, such as: Lack of data: There is a lack of data available to train AI systems, which limits their ability to accurately identify patterns in mental health conditions and predict outcomes.