Introduction

Peer review is a key component to the success of scientific articles. High-quality peer review ensures reliable information, improves the efficiency of communication, and facilitates the interpretation of findings for better applicability of the data presented in studies. Recently, with the increasing availability of artificial intelligence (AI) tools, especially large language models (LLMs), several scientific journals have published policies and guidelines regarding their use in the peer review process. LLMs are generative AI tools capable of producing text, figures, and other forms of content based on the datasets on which they have been trained. Their use in scientific publications has gained prominence in improving the quality of scientific writing, translating texts, reformatting references, and supporting peer review. There are still no clear guidelines on the use of LLMs in peer review. In 2024, Li et al. reviewed the editorial practices of 100 high-impact medical journals and found that 78% had clear guidelines on the use of AI in peer review. Among these, 59% (46 journals) explicitly prohibited the use of AI in generating reports, whereas 32 journals (41%) allow its use in a limited manner, provided that confidentiality was maintained and copyright protected. The objective of this article was to present a review on this topic and to propose a rationale for implementing AI in accordance with best practices in ethics and scientific integrity.

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In July 2020, Brazil has the world’s second highest Covid-19 death toll. The COVID-19 pandemic is spreading fast in America. Since the first case of COVID-19 was confirmed, it took 114 days in Brazil (February 26-June 19) and 98 days in the USA (January 21-April 29) for the number of cases to reach more than 1,000,000. Parallel to the rapid growth of COVID-19 cases, there has been a progression in the number of scientific publications. Until June 2020, more than 25,800 papers about COVID-19 were published in PubMed (17,800 open access – 69%). This volume of scientific publications is unprecedent. The Journal of the American Medical Association (JAMA), for example, received more than 11,000 submissions from January 1 to June 1, 2020, compared with approximately 4,000 submissions during the same period in 2019. The International Journal of Cardiovascular Sciences is facing a similar situation with more than doubled manuscripts submitted. Our aim is to discuss the importance of Open Science during the COVID-19 pandemic and the role of cardiology journals in this special moment.

COVID-19 is a new disease and many of its aspects are still obscure. What is known about SARS-CoV-2 transmission, incubation, and environmental stability? What are the risk factors for the disease? What is the best evidence-based therapy? What is the real importance of asymptomatic and presymptomatic virus shedding in SARS- CoV-2 transmission? How long neutralizing antibodies persist following infection, and do they confer immunity to reinfection? These and many other key questions are still unanswered. There is a great need for fast and efficient publication of information, but at the same time all efforts must be made to assure quality, and avoid biases and limitations.

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