The introduction of ChatGPT3 in 2023 disrupted the field of artificial intelligence (AI). ChatGPT uses large language models (LLMs) but has no access to copyrighted material including scientific articles and books. This review is limited by the lack of access to: (1) prior peer-reviewed articles and (2) proprietary information owned by the companies. Despite these limitations, the article reviews the use of LLMs in the publishing of scientific articles. The first use was plagiarism software. The second use by the American Psychological Association and Elsevier helped their journal editors to screen articles before their review. These two publishers have in common a large number of copyrighted journals and textbooks but, more importantly, a database of article abstracts. Elsevier is the largest of the five large publishing houses and the only one with a database of article abstracts developed to compete with the bibliometric experts of the Web of Science. The third use and most relevant, Scopus AI, was announced on 16 January 2024, by Elsevier; a version of ChatGPT-3.5 was trained using Elsevier copyrighted material written since 2013. Elsevier's description suggests to the authors that Scopus AI can write review articles or the introductions of original research articles with no human intervention. The editors of non-Elsevier journals not willing to approve the use of Scopus AI for writing scientific articles have a problem on their hands; they will need to trust that the authors who have submitted articles have not lied and have not used Scopus AI at all.

• MeSH
• periodika jako téma * MeSH
• psaní * MeSH
• publikování * MeSH
• umělá inteligence * MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

INTRODUCTION: Reporting guidelines were established to improve the quality of scientific papers. Currently, the most common are CONSORT (Consolidated Standards of Reporting Trials), STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and CARE (Clinical Consensus-based Case Reporting Guideline). Spin can be defined as (un)intentionally biased interpretation of results aimed at convincing readers of a positive benefit of any given intervention. The aim of the study was to evaluate the adherence of papers published in Perspectives in Surgery to CONSORT, STROBE or CARE and to identify the frequency of spin. METHODS: All articles published between 10/2014 and 9/2016 were analysed. Editorials and letters to editor were excluded. Original papers and case reports were assessed using 12 parameters. Any conclusion not corresponding to outcomes from pre-defined measurements was identified as spin. Descriptive statistics were used. RESULTS: Of 210 articles, 144 (69%) were analysed - 67 (47%) retrospective studies, 3 (2%) prospective studies and 74 (51%) case reports. The studies showed the highest compliance in terms of reporting the cohort size (89%). On the other hand, study limitations were presented in 22%. Performed investigations and interventions were described in all (100%) case reports. Conversely, limitations were not mentioned in any. None of the analysed papers met all of the 12 monitored parameters. Spin was identified in 47 (67%) original articles. CONCLUSION: None of the evaluated papers adhered completely to the current reporting guidelines. Spin occurred in more than 2/3 of the publications.Key words: reporting guidelines spin CONSORT STROBE CARE.

• MeSH
• chirurgie * MeSH
• dodržování směrnic * MeSH
• lidé MeSH
• prospektivní studie MeSH
• publikování * MeSH
• retrospektivní studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE: The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS: Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS: Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION: Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.

• MeSH
• farmakovigilance * MeSH
• léčivé přípravky MeSH
• lidé MeSH
• nežádoucí účinky léčiv * epidemiologie   MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH
• Názvy látek
• léčivé přípravky MeSH