Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed.

Background: The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey.

Background: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences.

Methods: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery.

The Impact of Parental Electronic Health Literacy on Disease Management and Outcomes in Pediatric Type 1 Diabetes Mellitus: Cross-Sectional Clinical Study.

Background: Despite the growing uptake of smart technologies in pediatric type 1 diabetes mellitus (T1DM) care, little is known about caregiving parents' skills to deal with electronic health information sources.

Objective: We aimed to assess the electronic health literacy of parents caring for children with T1DM and investigate its associations with disease management and children's outcomes.

Methods: A cross-sectional survey was performed involving 150 parent-child (8-14 years old with T1DM) dyads in a university pediatric diabetology center.

Psychometric Properties of General Oral Health Assessment Index Across Ages: COSMIN Systematic Review.

Objectives: To systematically review the psychometric properties of the Geriatric Oral Health Assessment Index (GOHAI) across age groups using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology.

Methods: Data: English peer-reviewed articles reporting studies of the development, translation, or validation of GOHAI.

Sources: PubMed, Web of Science, and EMBASE from Jan 1990 until December 31, 2023.

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.

Background: Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine.

Objective: We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies.