Despite the potential of AI for anatomy identification and diagnosis of oral clinical and radiographic conditions raised in the previous chapters, several important professional considerations and limitations should also be discussed in regard to the dependable deployment of dental AI in routine practice. This chapter therefore aims to propose practical guidelines for responsible translation, focusing on important domains such as: (1) data quality constraints, ground-truth ambiguity, and labeling variability; (2) bias, demographic representativeness, and fairness; (3) generalizability threats including dataset shift and inadequate external validation; (4) calibration, uncertainty communication, and clinically defensible operating thresholds; (5) explainability limits, human factors, automation bias, and trust calibration; (6) performance-metric pitfalls and the distinction between discrimination and clinical utility; (7) the need for prospective studies, workflow integration research, and outcome-focused evaluation; (8) regulatory and standards requirements for software as a medical device (SaMD), quality management, and post-market surveillance; (9) interoperability and infrastructure barriers in dental information systems; (10) privacy, cybersecurity, and federated learning considerations; and (11) medico-legal accountability and governance for monitoring model drift over time.