Using AI to enhance, not replace, human skill in knowledge communication
Author: Dr Tara Crandon
JBI, Faculty of Health and Medical Sciences, the University of Adelaide, Adelaide, SA, Australia
JBI considers evidence-based healthcare (EBHC) to be cyclical. When health professionals and patients call for specific needs to be met, evidence is generated to meet these needs in effective, feasible and meaningful ways. But how do we close this loop and share this knowledge with the people who most need it? Though the question seems simple, knowledge translation is an ongoing challenge. Evidence can oftentimes be complex, niche and inaccessible to people from a diverse range of backgrounds. Failing to communicate knowledge in a way that can be understood can mean that the impact and relevance of the work gets lost in translation.
Collaboration with AI in knowledge communication
In light of these challenges, many who work across science and practice are turning to creative and innovative methods to communicate knowledge. Large language models (LLM) are revolutionising the scientific world (Altmae et al., 2023; Kwon, 2024). The adoption of artificial intelligence (AI) and automated tools is proliferating and people are using these technologies in all corners of the evidence ecosystem, from evidence generation, synthesis, transfer and communication, to implementation (Nilsen et al., 2024; Naylor et al., 2025).
Scientific publishing, an important avenue for knowledge dissemination, is not immune to AI. At JBI Evidence Synthesis, one of JBI’s two society journals, we are seeing how these tools could be applied throughout different phases of the editorial process. For authors, AI can be used to flag grammar and spelling inconsistencies, refine language so that it is more accessible to a wider audience, support the creation of figures or graphs and help authors in making difficult decisions, like reducing word count. Tools such as Piktochart AI or Graphy AI can be used to transform complex data into diagrammatic format (e.g. charts, graphical abstracts, infographics) which can make knowledge more accessible to readers from non-technical backgrounds. Using AI in these ways can be particularly beneficial for authors with limited funding and resources, who are from a low- and middle-income country (LMIC) or for those who face other systemic barriers (e.g. researchers with disabilities).
For editors, automation can support data handling and administrative processes, such as detecting duplications or checking for plagiarism. When embedded into programs like Editorial Manager or Turnitin, these tools can be leveraged to significantly reduce workload (Liu, 2023; Kwon, 2024). Opportunities to use AI in the peer review process are also being discussed, particularly in using generative AI to help develop peer review reports (Leung et al., 2023). This is a topic gaining interest globally, with the chosen theme of Peer Review Week (PRW) being ‘Rethinking Peer Review in the AI Era.’ Keen readers of JBI Evidence Synthesis may have noticed our recent contribution to this topic, in celebration of PRW. Proponents of using these technologies in peer review argue that it could help to enhance efficiency and make the experience more engaging for reviewers, which can have flow-on effects for the knowledge communication pipeline.
Challenges of using AI to communicate knowledge
Of course, much of the global research community continues to pose questions on the role of AI in EBHC (Dupps, 2023). A key topic of ongoing debate is how to best use these tools responsibly and ethically. Generating content with AI is especially fraught with challenges. LLMs are also known to generate content based on training data, which can be ‘scraped’ or extracted from existing digital sources. This inevitably raises concerns around plagiarism and credibility, particularly if authors fail to sufficiently review, verify and transform AI-generated content (Kwon, 2024; Liu, 2023). Furthermore, using tools like ChatGPT to write manuscripts carries ambiguity around how to attribute authorship and intellectual property. For editors and peer reviewers, inputting work into AI could breach confidentiality and risk information leakage (Leung et al., 2023).
Many in the global scientific community are arguing that because of such concerns, any AI use should be fully disclosed (Kwon, 2024; Leung et al., 2023). Those involved in scientific publication should be guided by principles of accountability, transparency and confidentiality. At JBI Evidence Synthesis, we endorse this approach and have now implemented a screening question for new submissions that allows authors to communicate if and how AI was used.
A consensus on how to fully address the challenges presented by AI is yet to be reached, though guidelines on AI use in evidence synthesis are pending. In the meantime, the qualities that AI lacks and that make us distinctly human cannot be overemphasised (Nilsen et al., 2024). Discernment, expertise and empathy, to name a few, are skills especially necessary for communicating EBHC.
The human touch
Those in the EBHC community may know that JBI aims to promote and support evidence-based decisions that improve health and health service delivery. The cornerstone of EBHC is comprehensive, rigorous evidence synthesis, such as systematic reviews (Nilsen et al., 2024). Good-quality reviews are not only methodologically sound but are presented in a way that is digestible for health professionals and policymakers. At JBI Evidence Synthesis, we are committed to supporting authors in publishing reviews that are meaningful, creative, interesting and importantly address identified healthcare needs.
At the heart of this is evidence that is generated for and by, humankind. Humans are complex and diverse, shaped by their surrounding environments and the systems in which they live. Individual preferences, values, social relationships, communities, culture, spirituality and broader influences like healthcare or the government, can affect how humans share and communicate knowledge. Part of the art of knowledge translation is having an intimate understanding of what it is to be human.
Our strengths at JBI Evidence Synthesis
Human collaboration
For those unfamiliar with JBI Evidence Synthesis, our editorial process ensures manuscripts pass through multiple human hands. Papers submitted to JBI Evidence Synthesis are screened for interest and potential to influence practice, with less emphasis on formatting and language. Not only does this help lower barriers for authors but it also allows us to focus on the topic and its relevance to the healthcare community. Our editorial team discuss each review to ensure a fair, equitable process and to share ideas on refining work to enhance readability, accessibility and impact. At JBI Evidence Synthesis, we view the peer review process as a collaboration between authors, peer reviewers and our editorial staff, wherein the goal is to disseminate the best available evidence to improve patient outcomes globally. The wider editorial board at JBI Evidence Synthesis help to guide our decisions, bringing diverse expertise, cultural awareness, lived experience and the ability to interpret nuance and context for specific disciplines. We also invite broader conversation with our editorial board around our processes and best practices, to ensure the experience is convenient and most supportive of authors.
Within our team are experienced in-house editors, who have a keen eye for detail and have immense expertise at refining wording and softening complex scientific language. Our commitment to sharing voices from all regions and perspectives is evident in our initiative to offer authors the opportunity to submit a translation of their review abstract if members of the team are proficient in a language other than English. Collaborating with authors to ensure that knowledge is communicated in different formats is key to improving accessibility and helps to facilitate global collaboration on science and practice. To date, we are pleased to have published abstracts in a range of languages such as Danish and Japanese.
Outside of the editorial process, JBI has built a suite of digital resources that provide researchers with the knowledge needed to conduct high-quality reviews. Some examples include the JBI Manual for Evidence Synthesis and our educational videos, which we aim to continue expanding upon. In addition to these resources, we provide templates for a diverse range of review types, providing examples how authors can communicate and structure their work. We hope this helps authors to understand what details are needed to clearly and comprehensively describe their work to maximise its impact.
Along with the broader EBHC community, we at JBI Evidence Synthesis are continuing to learn about how to integrate AI and automated tools into our workflow. What is becoming increasingly clear is that these technologies should be used to enhance how we communicate knowledge, not replace the strengths of human collaboration.
References
Altmae, S., Sola-Leyva, A., Salumets, A. (2023). Artificial intelligence in scientific writing: A friend or a foe? Reproductive BioMedicine Online, 47(1): 3-9. https://doi.org/10.1016/j.rbmo.2023.04.009
Dupps, W. J. (2023). Artificial intelligence and academic publishing. Journal of Cataract & Refractive Surgery, 49(7): 655-656. https://doi.org/10.1097/j.jcrs.0000000000001223
Guldager, R., Nordentoft, S., Poulsen, I., Aadal, L., Loft, M. I. (2023). Wants and needs for involvement experienced by relatives of patients with an acquired brain injury: a scoping review. JBI Evidence Synthesis, 21(5). https://doi.org/10.11124/JBIES-22-00022
JBI Global. (2025). Celebrating Peer Review Week [internet]. Accessed September 25, 2025. Available from: https://jbi.global/news/article/celebrating-peer-review-week
JBI Global (n.d.). About JBI: Who are we? [internet]. Accessed September 25, 2025. Available from: https://jbi.global/about-jbi
Kwon, D. (2024). AI is complicating plagiarism. How should scientists respond? Nature [internet]. Accessed September 25, 2025. Available from: https://www.nature.com/articles/d41586-024-02371-z
Leung, T., de Azevedo Cardoso, T., Mavragani, A., Eysenbach, G. (2023). Best practices for using AI tools as an author, peer review, or editor. Journal of Medical Internet Research, 25: e51584. https://doi.org/10.2196/51584
Liu, L. (2023). The applications and challenges of artificial intelligence in the publishing industry. Geographical Research Bulletin, 2: 124-127. https://doi.org/10.50908/grb.2.0_124
Makimoto, K., Konno, R., Kinoshita, A., Kanzaki, H., Suto, S. (2023). Incidence of severe infection in patients with rheumatoid arthritis taking biologic agents: a systematic review. JBI Evidence Synthesis, 21(5). https://doi.org/10.11124/JBIES-22-00048
Naylor, N. R., Hummel, N., de Moor, C., Kadambi, A. (2025). Potential meets practicality: AI’s current impact on the evidence generation and synthesis pipeline in health economics. Clinical and Translational Science, 18(4): e70206. https://doi.org/10.1111/cts.70206
Nilsen, P., Sundemo, D., Heintz, F., Neher, M., Nygren, J., Svedberg, P., Petersson, L. (2024). Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare. Frontiers in Health Services, 11(4): 1368030. https://pmc.ncbi.nlm.nih.gov/articles/PMC11196845/
Peer Review Week. Rethinking Peer Review in the AI Era [internet]. Accessed September 25, 2025. Available from: https://peerreviewweek.net/theme.php
Links to additional resources
COPE. The membership organisation for publication ethics [Internet]. Accessed September 25, 2025. Available from: https://publicationethics.org/
Graphy. The AI Graph Maker [Internet]. Accessed October 8, 2025. Available from: https://graphy.app/
JBI Global (2024). JBI Manual for Evidence Synthesis [Internet]. Accessed September 25, 2025. Available from: https://jbi-global-wiki.refined.site/space/MANUAL/355827730/Table+of+Contents
JBI Evidence Synthesis (n.d.). Video Gallery [Internet]. Accessed September 25, 2025. Available from: https://journals.lww.com/jbisrir/Pages/videogallery.aspx
Piktochart. Meet the Next Generation of Infographics [Internet]. Accessed October 8, 2025. Available from: https://piktochart.com/?nab=1
To link to this article - DOI: https://doi.org/10.70253/BAHM9861
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The views expressed in this World EBHC Day Blog, as well as any errors or omissions, are the sole responsibility of the author and do not represent the views of the World EBHC Day Steering Committee, Official Partners or Sponsors; nor does it imply endorsement by the aforementioned parties.
