Observational Diagnostics: The Building Block of AI-Powered Visual Aid for Dental Practitioners.

Artificial intelligence (AI) has gained significant traction in medical image analysis, including dentistry, aiding clinicians in making timely and accurate diagnoses. Radiographs, such as orthopantomograms (OPGs) and intraoral radiographs, along with clinical photographs, are the primary imaging modalities employed for AI-powered analysis in the dental field. In this review, we discuss the most recent research and product developments concerning the clinical application of AI as a visual aid in dentistry and introduce the concept of Observational Diagnostics (ODs) as a structured method to standardise image analysis.

Estimating the Benefits of Oral Cancer Screening: Challenges and Opportunities.

Screening is widely believed to facilitate early detection and improve health outcomes; however, recent evidence questions its overall benefits, highlighting the need for a critical assessment of cancer screening programs. The simplicity of clinical oral examination and the easy accessibility of the oral cavity make oral cancer a suitable candidate for opportunistic screening. Despite this uniqueness, studies that solidly assess the efficacy and cost-effectiveness of oral cancer screening are scanty, and the only large randomized trial undertaken so far shows limited evidence of mortality reduction through population screening.

Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.

Objectives: Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature.

Deceptive Measures of "Success" in Early Cancer Detection.

Early detection of cancer is considered a cornerstone of preventive medicine and is widely perceived as the gateway to reducing cancer deaths. Based on this assumption, large trials are currently underway to evaluate the accuracy of early detection tests. It is imperative, therefore, to set meaningful "success criteria" in early detection that reflect true improvements in health outcomes.