The creation of various types of AI has led to the generation of a large cluster of data, called “Big Data”. This data storage brings countless possibilities for use, such as in health and education, data interpretation, recognition of writing patterns, and analysis and construction of texts.⁸ The social explosion of AI occurred in November 2022, with the launch of the chatbot ChaGTP, developed by OpenAi, which was the first use of generative AI to be successful and reach millions of users quickly. Generative AI consists of algorithms that, once trained with extensive databases, can provide original texts, images, and audios, based on learned patterns.⁹

Currently, the use of high-performance software, chatbots and virtual assistants permeates society and impacts all spheres of human thought thanks to the constant development of NLP, computer vision and robotics.² However, along with these advances come ethical and social issues that need to be addressed, such as privacy, security, the use of original data created by third parties, plagiarism and the impact on the job market.

Chat GTP and similar models have potential applications in several areas due to the ability to process large volumes of data, review languages and literature, summarize texts, correct spelling and grammatical errors and even write scientific articles. However, its results are still imprecise, and its limitations on a given topic can compromise data reliability and lead to significant errors.¹⁰ Besides, the indiscriminate use of generative AI can lead to a decrease in the creativity of authors, since AI is limited to the database that was inserted for its training and its everyday use can make authors dependent on this model.¹¹

It is important to know that the use of generative AI is associated with the risk of disclosing personal and confidential data, as several AI platforms make the information entered into them accessible to the public domain,¹¹ Therefore, published data, even intellectual property of the author, will be considered plagiarism because it is available in an IA.¹⁰

Generative AI, despite being used by millions of people around the world, also has limitations. This modality is not capable of understanding commands and responses, given that its responses come from language standards on the internet.¹¹ Therefore, generative AI can reproduce dominant social views and compromise the opinions of minorities, further reinforcing pre-existing prejudices in society.¹¹

In the scientific-academic context, the smart database technology has been part of the daily lives of researchers, students and teachers in Brazil. Due to the lack of consensus on the use of AI in publications and dilemmas faced by scientific journals, there is an urgent need for a broad discussion on its use, as shown in Figure 1.¹²

Figure 1

The most promising contribution of AI to scientific publications is the high capacity for data analysis.⁷ It is difficult to observe new discoveries in the large volume of articles published around the world each year using traditional database search methods. AI can be easily applied to mine, analyze and select data, identifying possible gaps in the literature, highly relevant citations and even self-plagiarism in works.¹³

For authors who want to get their first article and still don't know where to start, AI can provide help in organizing thoughts, generating written summaries, editing spelling errors, reviewing texts, suggesting improvements in writing and even assisting in preparation of figures.¹⁴ Furthermore, AI can be used to translate texts into different languages, facilitating the global dissemination of knowledge.

In addition, AI can become a strong ally for editors, especially in the process known as peer review, by ensuring that articles are of excellent quality and that their content is accurate and reliable.¹⁵ Also, AI can help identify potential reviewers based on their area of expertise, and detect the originality of works submitted to the journal.

In the process of choosing a journal to submit a scientific article, AI can assist both the authors by analyzing the journals’ most common publication themes and metrics and editors-in-chief by predicting the number of citations that an article may receive based on various metrics and characteristics of the article.

The integration of AI in scientific publications has revolutionized the way research is conducted, published, reviewed and disseminated. Despite the challenges, such as the need to ensure transparency and ethics at all stages, the potential benefits are vast, as shown in Figure 2. Ongoing collaboration between AI researchers and developers is essential to maximize these benefits and drive significant advances at the frontier of scientific knowledge.

Figure 2