SUMMARY
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of
biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology
during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods
(especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of
medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of
artificial neural networks in medical diagnosis through selected examples.
KEY WORDS
medical diagnosis; artificial intelligence; artificial neural networks; cancer; cardiovascular diseases; diabetes
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