TY  - GEN
AU  - Amato, F.
AU  - López, A.
AU  - Peña-Méndez, E.M.
AU  - Vaňhara, P.
AU  - Hampl, A.
AU  - Havel, J.
T1  - Artificial neural networks in medical diagnosis
LA  - eng
PY  - 2013
SP  - 47
EP  - 58
T2  - Journal of Applied Biomedicine
SN  - 1214-0287
VL  - 11
IS  - 2
PB  - University of South Bohemia
PP  - Ceske Budejovic
AB  - 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. © Journal of Applied Biomedicine.
DO  - 10.2478/v10136-012-0031-x
UR  - https://portalciencia.ull.es/documentos/5e3c38f729995246bbf5f05b
DP  - Dialnet - Portal de la Investigación
ER  -