The objective of this study was to develop and validate an App for identifying risk factors for oral cancer. To this end, we developed an App (OCS: Oral Cancer Screening) with predictors of Oral Cancer (OC) and algorithm assembly to estimate the risk of its development. Methodology: Simulated clinical cases were designed so that 40 professionals with expertise in oral diagnostics could validate the algorithm and test its usability (SUS: System Usability Score) and acceptability (TAM: Technology Acceptance Model). Cronbach's alpha coefficient, Friedman/Dunn tests, and Spearman correlation evaluated the SUS and TAM scales. ROC curve was plotted to estimate the cutoff point of the algorithm in suggesting a high risk for OCS of the simulated cases. Chi-square and Fisher's exact tests were additionally used (p<0.05, SPSS v20.0). Results: Professionals with expertise in oral diagnosis had usability of 84.63±10.66 and acceptability of 84.75±10.62, which correlated positively (p<0.001, r=0.647). Acting in clinical areas of dentistry (p=0.034) and history of performing OC risk factor orientation (p=0.048) increased acceptability while acting in higher education increased usability (p=0.011). The cutoff point suggested by the App after validation of the simulated clinical cases showed high sensitivity of 84.8% and lower specificity of 58.4%. Conclusion: The OCS was effective and with adequate sensitivity, usability, and acceptability and may contribute to the detection of early oral lesions.

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