AIMC Topic: Guidelines as Topic
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Protocol for development of a checklist and guideline for transparent reporting of cluster analyses (TRoCA).
BMJ open
Aug 21, 2025
INTRODUCTION: Cluster analysis, a machine learning-based and data-driven technique for identifying groups in data, has demonstrated its potential in a wide range of contexts. However, critical appraisal and reproducibility are often limited by insuff...
Reporting guideline for Chatbot Health Advice studies: the CHART statement.
BMC medicine
Aug 1, 2025
BACKGROUND: The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clini...
Journal of Global Health's Guidelines for Reporting Analyses of Big Data Repositories Open to the Public (GRABDROP): preventing 'paper mills', duplicate publications, misuse of statistical inference, and inappropriate use of artificial intelligence.
Journal of global health
Jul 1, 2025
In recent years, global accessibility to large 'big data' repositories that enable 'open research' - such as the UK Biobank, National Health and Nutrition Examination Survey (NHANES), and Global Burden of Disease (GBD) datasets - has created unpreced...
Large Language Models and the Analyses of Adherence to Reporting Guidelines in Systematic Reviews and Overviews of Reviews (PRISMA 2020 and PRIOR).
Journal of medical systems
Jun 12, 2025
In the context of Evidence-Based Practice (EBP), Systematic Reviews (SRs), Meta-Analyses (MAs) and overview of reviews have become cornerstones for the synthesis of research findings. The Preferred Reporting Items for Systematic Reviews and Meta-Anal...
Development of Evidence-Based Guidelines for the Integration of Generative AI in University Education Through a Multidisciplinary, Consensus-Based Approach.
European journal of dental education : official journal of the Association for Dental Education in Europe
Feb 13, 2025
INTRODUCTION: The introduction highlights the transformative impact of generative artificial intelligence (GenAI) on higher education (HE), emphasising its potential to enhance student learning and instructor efficiency while also addressing signific...
Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals.
BMC medicine
Feb 11, 2025
BACKGROUND: Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of re...
Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations.
European journal of dental education : official journal of the Association for Dental Education in Europe
Jan 31, 2025
BACKGROUND: Generative AI (GenAI) tools like ChatGPT are increasingly relevant in dental education, offering potential enhancements in personalised learning and clinical reasoning. However, specific guidance from dental institutions remains unexplore...
Ophthalmology Journals' Guidelines on Generative Artificial Intelligence: A Comprehensive Analysis.
American journal of ophthalmology
Dec 30, 2024
PURPOSE: The integration of generative artificial intelligence (GAI) into scientific research and academic writing has generated considerable controversy. Currently, standards for using GAI in academic medicine remain undefined. This study aims to co...
Do Ophthalmology Journals Have AI Policies for Manuscript Writing?
American journal of ophthalmology
Nov 7, 2024
PURPOSE: To assess the prevalence of artificial intelligence (AI) usage policies in manuscript writing in PubMed-indexed ophthalmology journals and examine the relationship between the adoption of these policies and journal characteristics.
Inhalation Toxicity Screening of Consumer Products Chemicals using OECD Test Guideline Data-based Machine Learning Models.
Journal of hazardous materials
Aug 6, 2024
This study aimed to screen the inhalation toxicity of chemicals found in consumer products such as air fresheners, fragrances, and anti-fogging agents submitted to K-REACH using machine learning models. We manually curated inhalation toxicity data ba...
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