The Role of AI in Transforming Scientific Publications

Scientific publishing is evolving rapidly with the advent of AI, transforming content creation, data visualization, and workflow management. While pharma publications demand rigorous accuracy and compliance, AI is making the journey from data to publication faster and more efficient, and accelerating processes without compromising quality. However, it is vital to recognize at the outset that AI is a powerful tool designed to enhance—not replace—human expertise in the publication process.

Challenges in traditional scientific publishing

• Long timelines: Months can pass between data generation and a finalized manuscript, slowing the dissemination of key findings, which may impact patient outcomes.
• Manual labor: Literature reviews, referencing, and formatting are painstaking, manual tasks that drain valuable time and introduce the risk of error.
• Bottlenecks: Drafting publications; multiple review cycles; medical, legal, and regulatory (MLR) approvals; and compliance steps often slow down progress, delaying publications.
• Scaling issues: Expanding global publication plans to local/regional levels can put considerable strain on pharma teams and limit their ability to scale faster and disseminate information in a timely manner.

How AI enhances the publication lifecycle

1. Literature review & planning

AI-powered tools can accelerate literature searches, synthesize key findings, and help identify research gaps, expediting publication planning and grounding manuscripts in current evidence.

2. Drafting manuscripts

Using appropriate source material, AI can generate well-structured content across sections and maintain consistency, logical flow, and clarity throughout the manuscript. It can also recommend appropriate journal choices for submission depending on the novelty and relevance of the study results.

• Introduction: Can help draft concise introductions by summarizing background literature, highlighting knowledge gaps, and clearly stating objectives.
• Methods: Can assist in describing study design, populations, interventions, endpoints, and statistical analyses.
• Results: Can complement human intelligence by organizing and summarizing findings from data sets, suggesting logical flow and effective format for presentations, and highlighting statistically significant outcomes, while the writer still validates these AI-generated insights, ensuring proper interpretation within scientific context, and maintaining the integrity of published research.
• Discussion: Can help structure the discussion by summarizing key results, comparing study results with existing literature with directional help from the writer, and outlining implications and future research directions. However, subject matter expertise is crucial to interpret results within the broader scientific and clinical context, critically analyze limitations, and craft a nuanced, authoritative discussion.
• Conclusion: Can summarize the main findings and their significance, ensuring a clear and impactful conclusion.

3. Abstracts, summaries, and plain language summaries (PLS)

• De novo abstracts: Can analyze a full manuscript or CSR/protocol and datasets to generate a draft version of a new abstract tailored to journal or congress guidelines.
• Encore/adapted abstracts: Can adapt original abstracts for new venues, ensuring compliance and relevance for different audiences. It can rephrase and restructure abstracts to highlight specific data points or perspectives for various submission types.
• Summaries and plain language summaries (PLS): Can generate concise summaries and PLS, making complex scientific information accessible to non-specialist audiences, patients, and the public. AI can simplify language, clarify key points, and tailor content for different levels of health literacy.

4. Tables and figures

AI can automate the creation of tables and charts, reducing manual efforts and errors, and ensuring compliance with journal/congress guidelines.

5. Editing, quality control, and journal submissions

AI-powered tools can check grammar, style, tone, and consistency (brand/terminology); automate reference management; and ensure that formatting meets the final journal requirements, streamlining the editing process and reducing errors. It can also assess the manuscript’s submission readiness by checking its technical compliance with the journal’s submission guidelines. This can ensure a high first-pass acceptance or lower rejection rate for AI-assisted submissions. For example, manuscripts pre-checked with Paperpal Preflight had a 15.5% lower initial rejection rate compared to those without pre-checking.

Limitations of AI

While AI offers speed and efficiency, it is not without pitfalls. Responsible use of AI requires a clear understanding of its limitations:

• Factual inaccuracy: AI may fabricate data or references, especially when prompts are vague or poorly defined.
• Lack of clinical nuance: AI lacks the ability to judge/interpret clinical nuances that influence scientific narratives.
• Validation risk: Over-reliance on AI outputs without expert verification can compromise scientific accuracy and regulatory compliance.
• Bias reinforcement: If trained on incomplete or skewed data, AI can amplify existing biases in literature or analysis.

Need for human-in-the-loop workflows

Integrating AI tools into human-in-the-loop workflows is essential to ensure accuracy, quality, and ethical compliance. Human oversight at key stages—such as drafting, reviewing, and validating—helps identify errors, uphold scientific rigor, and prevent the propagation of inaccuracies or biases. These workflows protect the integrity and reliability of AI-assisted publications.

Ethics, transparency, and regulatory guidance

The International Committee of Medical Journal Editors (ICMJE) and Good Publication Practice (GPP 2022) set clear standards:

• Mandatory disclosure: Authors must disclose any use of AI-assisted technologies in the manuscript, cover letter, or acknowledgments, depending on the context. Disclaimers should, however, not serve the purpose of discharging the responsibility to the AI-assisted tools, and authors must still take full responsibility for content correctness.
• Example disclosure statement: “AI-powered tools (e.g., ChatGPT) were used to assist with literature search, initial drafting, language editing, and plain language summary creation. All outputs were reviewed and validated by the authors to ensure scientific accuracy and compliance with ethical standards.”
• AI is not an author: AI tools cannot be credited as authors, as they cannot take responsibility for the work. Human authors must review, validate, and be accountable for all content.
• Ethical oversight: All AI-generated content must be carefully reviewed for accuracy, completeness, and potential bias. AI should not be used for data manipulation, image fabrication, or generating unverified references.
• Transparency and attribution: Both ICMJE and GPP 2022 emphasize transparency in reporting AI’s role and ensuring that all sources, including AI-generated material, are properly attributed and checked for plagiarism.
• Reviewer and editor guidance: Editors and peer reviewers must also exercise caution when using AI, ensuring confidentiality and seeking appropriate permission before uploading manuscripts to AI platforms.
• Legal framework: While AI application in publications is widely accepted, its use for therapeutic guidance or clinical decisions is considered unethical and illegal in many jurisdictions. Physicians must retain full accountability for all patient care and treatment-related determinations.

The road ahead: Human + AI = Better science

AI is a powerful enabler and a productivity booster, transforming scientific publishing at an unprecedented pace.

Still, human expertise remains essential for

• Critical thinking: Evaluating AI-generated content for methodological soundness, logical consistency, and contextual appropriateness.
• Interpretation: Determining whether AI-assisted analyses accurately reflect the underlying data and for placing findings within the proper scientific framework.
• Maintaining the integrity and trustworthiness of scientific literature: Ensuring that AI contributions are properly disclosed and validated, and that the fundamental standards of scientific rigor are preserved in an era of increasing automation.

It is an era of collaboration between human ingenuity and intelligent tools.

By embracing AI now, pharma teams and authors, together with their MedComms partners, can:

• Speed up content creation and do so more affordably
• Enhance accuracy and ensure compliance with guidelines
• Increase accessibility of scientific content to patients without delays
• Support global publication strategies and assist Medical Affairs teams to scale faster

Pharma teams and authors must partner with agencies that demonstrate responsible AI implementation rather than those promising overnight generation of publications without the requisite content expertise. Organizations should engage agencies with robust scientific publications expertise that strategically integrate AI to enhance their services while maintaining expert oversight to ensure accuracy and regulatory compliance.

In summary

AI is revolutionizing content creation, but ethical use, transparency, and human oversight are vital to upholding quality and credibility in scientific publications. The future of scientific publishing will be powered by AI efficiency but governed by human expertise.