Background: Clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the United States. In the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. A considerable fact is that sensitivity and specificity of the fine needle aspiration varies significantly as it remains highly dependent on the operator as well as the cytologist’s skills. Practically, in the above group of patients, thyroid lobectomy/isthmusectomy becomes mandatory for attaining a definitive diagnosis where the majority (70%-80%) have a benign surgical pathology. The scattered nature of clinically gathered data and analysis of their relevant variables need a compliant statistical method. The artificial neural network is a branch of artificial intelligence. We have hypothesized that conduction of an artificial neural network applied to certain clinical attributes could develop a malignancy risk assessment tool to help physicians interpret the fine needle aspiration biopsy results of thyroid nodules in a context composed of patient’s clinical variables, known as malignancy related risk factors.Methods: We designed and trained an artificial neural network on a prospectively formed cohort gathered over a four year period (2007-2011). The study population comprised 345 subjects who underwent thyroid resection at Nemazee and Rajaee hospitals, tertiary care centers of Shiraz University of Medical Sciences, and Rajaee Hospital as a level I trauma center in Shiraz, Iran after having undergone thyroid fine needle aspiration. Histopathological results of the fine needle aspirations and surgical specimens were analyzed and compared by experienced, board-certified pathologists who lacked knowledge of the fine needle aspiration results for thyroid malignancy.Results: We compared the preoperative fine needle aspiration and surgical histopathology results. The results matched in 63.5% of subjects. On the other hand, fine needle aspiration biopsy results falsely predicted malignant thyroid nodules in 16% of cases (false-negative). In 20.5% of subjects, fine needle aspiration was falsely positive for thyroid malignancy. The Resilient back Propagation (RP) training algorithm lead to acceptable accuracy in prediction for the designed artificial neural network (64.66%) by the cross- validation method. Under the cross-validation method, a back propagation algorithm that used the resilient back propagation protocol - the accuracy in prediction for the trained artificial neural network was 64.66%.Conclusion: An extensive bio-statistically validated artificial neural network of certain clinical, paraclinical and individual given inputs (predictors) has the capability to stratify the malignancy risk of a thyroid nodule in order to individualize patient care. This risk assessment model (tool) can virtually minimize unnecessary diagnostic thyroid surgeries as well as FNA misleading.