Artificial intelligence in the preparation of scientific articles: uses, limits, and best practices

A Review of the TITAN Guideline: Advancing Transparency and Responsible Artificial Intelligence Use in Medical Research and Scholarly Publishing

Artificial Intelligence (AI) is increasingly being integrated into medical research and scholarly publishing, supporting activities such as literature searching, data analysis, medical imaging, manuscript preparation, and peer review. Despite these opportunities, AI use introduces concerns related to hallucinations, bias, privacy, confidentiality, copyright, reproducibility, and scientific accountability. Existing guidance provides important principles for responsible AI use, but reporting practices remain variable. This article reviews the TITAN guidelines as a practical framework for improving transparency and responsible reporting of AI use in medical research and scholarly publishing. The TITAN guidelines provide a proportionate and technology neutral approach to AI reporting. It distinguishes minor uses, such as language assistance, from substantive applications involving research design, analysis, interpretation, or scientific content. The framework emphasizes that human researchers retain responsibility for evaluating AI outputs and the integrity of published work. It also provides a basis for authors, reviewers, editors, and publishers to incorporate standardized AI reporting into scholarly workflows. TITAN offers a practical framework for documenting meaningful AI involvement while supporting transparency, reproducibility, and human accountability. Its flexible structure can accommodate emerging AI technologies, including multimodal and agentic systems. Periodic revision and coordinated adoption by medical journals and research communities will be important to maintain its relevance as AI capabilities continue to evolve.

The Role of Artificial Intelligence in the Lifecycle of Scientific Manuscripts: Authoring, Reviewing, and Editorial Selection

A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical verification and efficiency, while judgments on scientific merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.

Guidelines on the use of Artificial Intelligence for authors, reviewers, and editors of APJCN.

Artificial Intelligence in Medical Publishing: At the Crossroads of Innovation, Scientific Integrity, and Editorial Governance

Artificial intelligence is reshaping medical publishing through three complementary technologies: generative artificial intelligence, AI-powered evidence-synthesis platforms, and increasingly autonomous AI assistants.

AI and Academic Publication: The Good, the Bad, and the Ugly

Artificial intelligence has entered academic publication. It is already present in drafting, editing, translation, formatting, reviewing, and manuscript preparation. It is used by students, early-career researchers, senior scholars, reviewers, editors, and publishers. Some uses are minor. Some are substantial. Some are legitimate. Some are not. The task for journals is therefore not to pretend that AI can be excluded from scholarly writing. The task is to decide how it should be used, disclosed, governed, and judged. Academic writing has always involved tools. Authors use word processors, grammar checkers, reference managers, statistical software, translation programmes, plagiarism-detection systems, and journal submission platforms. These tools have changed the mechanics of writing and publication. Generative AI changes more than mechanics. It can produce fluent prose. It can summarise complex material. It can imitate disciplinary styles. It can create abstracts, titles, responses to reviewers, tables, outlines, and plausible literature overviews. It can also invent references, distort arguments, conceal weak reasoning, and produce text that appears scholarly without being scholarly. This editorial offers a simple position. AI is not an author. AI is not a scholar. AI is not a source of academic responsibility. It is a tool. Used well, it can support clarity, accessibility, and editorial preparation. Used poorly, it can damage quality. Used dishonestly, it can threaten trust in academic publication. The distinction matters. A journal should not treat all AI use as misconduct. Nor should it treat all AI use as harmless assistance. The proper standard is human accountability.

10 insights for harnessing artificial intelligence in scientific research: supervisor perspectives

Background Artificial intelligence (AI) transforms scientific research and publication by supporting all aspects of research from setting up hypothesis and research objectives to data analysis and manuscript preparation. Its widespread use in postgraduate research by students improves efficiency and feedback, but also raises concerns related to academic integrity, ethical behavior, algorithmic bias, hallucinated outputs, copyright issues, and mainly, preservation of students’ critical thinking skills. This article presents supervisor-oriented guidance for the responsible integration of AI into scientific research supervision. Methods We have undertaken narrative synthesis based on published manuscripts as well as our collective supervisory experience with AI-assisted research projects. Published literature evidence and practical experience were integrated to develop 10 insights for supervisors guiding research students who use AI tools during all aspects of their research. Results We have generated 10 practical insights for supervisors on guiding research students with ethical AI use: Establishing transparency and accountability; Setting expectations; Creating a customised AI use agreement; Maintenance of AI Use Log for Review; Reviewing AI log – The supervisor’s role; Addressing data privacy and ethical risks; Assessment of students’ critical thinking and conceptual understanding; Guide verification and validation of AI-generated content; Prepare students to justify AI-related decisions during post-research evaluation; and Institutional support for supervisor competency. Together, these recommendations emphasize the importance of human oversight, documentation and verification to distinguish AI assistance and original student scholarship. Conclusions AI can enhance postgraduate research and supervision when used as a transparent and ethically governed tool rather than as a shortcut. Supervisors have a central role in ensuring students’ ethical research practices evident by clear documentation of AI use, verification and transparency. A structured supervisory approach following the 10 insights can help balance the efficiency of AI tools with research integrity, accountability, and meaningful human mentorship.