Background: Quality assessment of diagnostic accuracy studies (QUADAS), and more recently QUADAS-2, were developed to aid the evaluation of methodological quality within primary diagnostic accuracy studies. However, its current form, QUADAS-2 does not address the unique considerations raised by artificial intelligence (AI)-centered diagnostic systems. The rapid progression of the AI diagnostics field mandates suitable quality assessment tools to determine the risk of bias and applicability, and subsequently evaluate translational potential for clinical practice.
View Article and Find Full Text PDFBackground Clinical Artificial intelligence (AI) has reached a critical inflection point. Advances in algorithmic science and increased understanding of operational considerations in AI deployment are opening the door to widespread clinical pathway transformation. For surgery in particular, the application of machine learning algorithms in fields such as computer vision and operative robotics are poised to radically change how we screen, diagnose, risk-stratify, treat and follow-up patients, in both pre- and post-operative stages, and within operating theatres.
View Article and Find Full Text PDFObjectives: To assess treatment options for the reconstruction of the lost interdental papilla and to evaluate evidence for their efficacy.
Methods: An electronic search (Medline, Embase and the Cochrane Library Database and OpenGray) and a hand search were carried out to identify all types of studies investigating interdental papilla reconstruction (except for reviews) with a minimum of 3 months follow-up.
Results: Forty-five studies were included in the study including 7 RCTs, 2 cohort studies, 19 case series and 17 case reports.