Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool

ORIGINAL ARTICLES Epidemiology Biostatistics and Public Health - 2020, Volume 17, Number 2Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA toolInvestigation of diagnostic value of artificialintelligence systems in the diagnosis of breastcancer based on histopathological imagesusing Meta-MUMS DTA toolABSTRACTBackground: Various artificial intelligence systems are available for diagnosing breast cancer based onhistopathological images. Assessing the performance of existing methodologies for breast cancer diagnosis is vital.Methods: The SCOPUS database has been searched for studies up to December 15, 2018. We extracted the data,including "true positive," "true negative," "false positive," and "false negative". The pooled sensitivity, pooled specificity,positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve of summary receiveroperating characteristic curve were useful in assessing the diagnostic accuracy. Egger's test, Deeks' funnel plot, SVE(Smoothed Variance regression model based on Egger’s test), SVT (Smoothed Variance regression model based onThompson’s method), and trim and fill methodologies were essential tests for publication bias identification.Results: Three studies with eight approaches from thirty-seven articles were found eligible for further analysis. Asensitivity of 0.95, a specificity of 0.78, a PLR of 7525, an NLR of 0.06, a DOR of 88.15, and an AUC of 0.953showed high significant heterogeneity; however, the reason was not the threshold effect. The publication bias wasdetected by SVE, SVT, and trim and fill analysis.Conclusion: The artificial intelligent (AI) systems play a pivotal role in the diagnosis of breast cancer usinghistopathological cell images and are important decision-makers for pathologists. The analyses revealed that theoverall accuracy of AI systems is promising for breast cancer; however, the pooled specificity is lower than pooledsensitivity. Moreover, the approval of the results awaits conducting randomized clinical trials with sufficient dat

Evaluation of patient satisfaction of the status of appointment scheduling systems in outpatient clinics: Identifying patients' needs

Appointment scheduling systems are potentially useful tools for enhancing the patient satisfaction. This study was conducted to inspect patient's needs and satisfaction of the current status of appointment scheduling systems in outpatient clinics. This cross-sectional study was conducted in 10 outpatient clinics with different specializations. The outpatient clinics were selected based on the stratified randomization method. Data were collected using a questionnaire from December 2016 to March 2017. The questionnaire reliability was measured with the participation of 15 patients using the test-retest method. The content validity was also evaluated by 13 experts. A total of 319 patients completed the survey. The mean score of overall patient satisfaction and the patient satisfaction of the clinic environment were 6.73 ± 0.16 and 8.30 ± 0.12, respectively. The average waiting time was 64.2 ± 3.45 min. The service time took on an average 9.85 ± 0.37 min. The patient satisfaction of the clinic environment (P = 0.023), length of waiting time (P = 0.001), and duration of service time (P = 0.005) had a statistically significant association with overall patient satisfaction. Based on the results, the need for improving overall patient satisfaction score was felt. The patient satisfaction of waiting time, service time, and clinic environment had the greatest influence on overall patient satisfaction. Furthermore, it is recommended that a web-based appointment scheduling system should be implemented