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Yet these traditional AI tools are often constrained by rigid rulesets or prebuilt machine-learning models that excel in well-defined tasks. Rather than requiring each new scenario to be painstakingly coded, agentic models leverage expansive training data (in the form of foundation models) to adapt to new situations.
Text Analytics in Healthcare refers to the process of extracting meaningful insights from unstructured medical text, such as patient records, doctors notes, clinical trial data, and research articles. It uses AI capabilities like NLP and machinelearning to analyze, categorize, and interpret vast amounts of text-based healthcare data.
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