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.
Eventhough the artificial neural networks posses several advantages over medical diagnosis, Amato et al. have demonstrated the philosophy, capabilities, and limitations through selected examples of medical data REF .
Artificial neural networks in medical diagnosis
Journal of Applied Biomedicine
Medicine