Scientific research is a process that requires dedication and rigour, while seeking to go beyond the mere presentation of data. Its primary objective is to convince the scientific community of the validity of a hypothesis and provide solid evidence that will advance our knowledge. Against this background, artificial intelligence (AI) is emerging as an agent of change with the potential to transform the way scientific articles are conceived and developed.

However, integrating AI into research presents a number of challenges. Models such as ChatGPT, which are capable of generating human-like text, have raised ethical questions and highlighted the need for firm boundaries. Previous articles have even credited such models as authors.1 Publishers have been quick to react, establishing clear policies prohibiting this practice. The guidelines of the International Committee of Medical Journal Editors2 (ICMJE) and the World Association of Medical Editors3 (WAME) stress the importance of transparency and accountability in the use of AI tools during manuscript writing. They also emphasise the need for effective tools to detect the use of AI in articles in order to preserve academic integrity.

The rise of AI in scientific writing has raised concerns about the integrity and validity of research. While AI tools offer the potential to create articles quickly and efficiently, they also pose significant risks that could undermine trust in science, including the ease of plagiarism, the incorporation of undetected bias, and the generation of false information. These challenges mean that we need to carefully assess how to integrate AI into research and establish safeguards to protect the quality and ethics of scientific knowledge.

The aim of this review is to explore the shifting landscape of scientific writing as it increasingly comes under the influence of AI. We examine key AI tools for their potential to optimise tasks such as information search, content generation, and text revision.

This section describes these tools and their uses. Table 1 provides a summary and links to these applications.

Chatbots are AI programmes designed to simulate human conversation and are transforming the way we interact with technology. They use Large Language Models (LLMs) trained on vast amounts of data. Chatbots simulate human conversations by generating and understanding language. Some examples of conversational chatbots include OpenAI's ChatGPT,4 Google's Gemini,5 and Microsoft's Copilot.6 They all use natural language processing and machine learning to generate human-like conversational text and maintain dialogue.7,8

However, chatbots have significant limitations. They can generate hallucinations and unverifiable text. While they are useful for basic tasks such as translation, greater potential can be unlocked by building custom chatbots. In addition to being trained with domain-specific data, these chatbots can be given detailed instructions to guide their behaviour and responses. This allows for greater control over the information they generate and the tasks they perform.

The search for accurate and truthful information is crucial in the production of scientific papers. To this end, customised chatbots can be strategic allies for authors. Tools such as Consensus and SciSpace, available via subscription to ChatGPT,4 exemplify the potential of these chatbots. Based on research and academic articles, they can provide accurate answers, including relevant references, by accessing and processing information from scientific article databases and receiving detailed instructions. All this facilitates the authors' tasks and improves the quality of their work.

However, if one is needed for a specific purpose, the best approach could be to create a custom chatbot. Platforms such as ChatGPT,4 Copilot,6 POE,9 or HuggingChat10 allow custom chatbots to be easily created. In this case, how the instructions for the chatbot are designed is of utmost importance. The following scheme is recommended for writing the instructions:

• •

  Task: Clearly and concisely define the goal or main idea.

• •

  Context: Provide the context or scope in which it will act.

• •

  Example: Include one or more representative examples of the desired interactions.

• •

  Persona: Specify the personality, style, and tone the chatbot should use. For example: “Act as a professor of pharmacokinetics, using simple and direct language, avoiding unnecessary technical jargon, and applying an academic tone.”

• •

  Format: Indicate the desired format, such as text, bulleted list, etc.

• •

  Tone: Set the general tone of the answers, whether formal, informal, academic, and so on.

Hospital pharmacy, like any other scientific field, is driven by the need to generate and disseminate up-to-date and accurate knowledge. In this context, AI is emerging as a tool with transformative potential, capable of assisting researchers at all stages of the scientific writing process. AI-based systems can optimise and streamline research, from searching and analysing the literature to reviewing and refining the final text. Thus, hospital pharmacy professionals can to dedicate more time to critical analysis and the interpretation of results.

However, the integration of AI in this field also raises ethical and methodological challenges that must be carefully considered to ensure responsible and effective use of these technologies. Concerns such as the introduction of bias, misinformation, privacy, lack of transparency, plagiarism, and authorship30 call for a responsible and cautious approach. Critical peer review is crucial to validate AI-generated data prior to its clinical application.

Although some publishers have expressed concern that AI will compromise the integrity of publications, a balanced approach would allow the benefits of AI to be harnessed without sacrificing quality. However, an additional challenge is the increasing difficulty in distinguishing AI-generated text from human-generated text. AI detection programmes are not infallible and can be circumvented by paraphrasing tools, increasing the risk that AI-generated articles will be mistakenly attributed to humans.31 It is therefore essential to develop robust strategies to ensure transparency and accountability in the use of AI in scientific writing.

A key set of measures must be implemented to facilitate the ethical and responsible use of AI in scientific writing. Transparency must be paramount, with clear guidelines established for the use of AI tools in research and publication. Peer review processes need to be strengthened to ensure thorough and rigorous evaluation of papers using AI. It is also crucial to invest in cutting-edge technology to monitor the use of AI, and to educate authors in ethical and responsible practices in this area. These measures will not only encourage innovation, but also maintain quality standards in academic publishing.

AI tools for science writing are evolving rapidly. This review has explored representative options in each category and shown their current potential. However, researchers should keep abreast of the new AI-based solutions that are constantly emerging.

Although AI-based tools can be of great help, not all of them are freely available. Some platforms require subscriptions or payments, which can restrict their use, particularly for researchers with limited budgets. A review of the alternatives allows us to make a better cost–benefit analysis of such tools. Hospitals with sufficient finding may opt for premium versions, whereas independent researchers or those with limited budgets may need to consider cheaper or free options. This risks further widening the resource gap between research institutions.

As noted by Scott,32 pharmacists must take a proactive role in building the future of the profession, anticipating and adapting to the changes that AI will bring. This entails becoming deeply involved in the development of AI solutions. The application of AI tools, such as those reviewed in this article, provides examples of how pharmacists can take advantage of these advances. However, it is crucial to maintain a critical stance to ensure that AI in academic and clinical settings is used responsibly and in line with our professional values. The integration of AI into hospital pharmacy represents an opportunity for pharmacists to take a leading role in transforming their practice, as described by Morillo et al.33 Far from being replaced by technology, pharmacists can leverage the power of AI to enhance their value within healthcare teams.

AI is transforming hospital pharmacy by providing tools that optimise research, scientific writing, and information management. Chatbots, research assistants, and literature mapping platforms streamline tasks and improve efficiency. However, it is critical to address ethical concerns such as bias and misinformation. Hospital pharmacists must take an active role in building the future of the profession by integrating AI ethically and effectively. This will empower their work and contribute to the advancement of scientific knowledge for the benefit of healthcare.

None declared.

Vicente Gimeno was responsible for the conceptualisation and methodology of the paper, the analysis of the AI tools, and the drafting of the manuscript, including its critical revision. Cristina Trigo contributed to the conceptualisation and supervision of the project and co-authored the original draft. Both authors played key roles in the design and execution of this study.

Vicente Gimeno-Ballester: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Cristina Trigo-Vicente: Writing – original draft, Validation, Supervision, Conceptualization.