Artificial Intelligence in Dental Radiology: Review of the Impacts on Diagnostic Accuracy and Nursing Practices - Qahman, Misfer Hussein Misfer; Almowallad, Raiyyah Abdulhdi; Alluqmani , Raniah Mohammed Rashed; Alluqmani , Rana Mohammed Rashed; Alghamdi , Rehab Saud; Al-Otaibi, Bandar Abdulaziz Tharban; Alshahrani , Abdullah Hussain Hassan; Alghamdi, Samer Mohammed (Journal of Ecohumanism, 2024)
Background: The use of artificial intelligence (AI) in dental radiography is revolutionizing diagnostic methodologies and nursing responsibilities within the healthcare domain. As AI technologies progress, comprehending their influence on diagnostic precision and nursing practices is crucial for future education and clinical implementation. Methods: A thorough literature analysis was performed, using papers from databases including PubMed, Scopus, and Google Scholar till 2023. The review concentrated on assessing the knowledge, attitudes, and competencies of healthcare, dental, and nursing students concerning AI applications in dental radiology. Results: The findings indicated that most of studies exhibited a moderate to strong comprehension of AI, accompanied by notable deficiencies in practical knowledge and skills. Although 65% exhibited favorable attitudes towards AI implementation in clinical environments, apprehensions regarding dehumanization and job displacement were widespread, particularly among students from disadvantaged areas. The majority of participants supported the incorporation of AI education into healthcare courses to improve comprehension and practical implementation. Conclusion: AI offers both advantages and obstacles in dental radiology, requiring a targeted educational strategy to prepare future healthcare practitioners with essential competencies. A curriculum that prioritizes practical applications, ethical considerations, and the collaborative function of AI in augmenting human capabilities is essential. Longitudinal studies are advised to track alterations in knowledge and attitudes following graduation and to evaluate the efficacy of AI training in enhancing clinical outcomes.