Scholarly Communication of Medical Knowledge and Digital Literacy in the AI for Science (AI4S) Paradigm

Dear Colleagues,

The integration of Artificial Intelligence for Science (AI4S) into biomedicine is instigating a structural reconfiguration of medical publishing that extends beyond mere workflow optimization. Unlike general scholarly communication, medical knowledge production is undergoing a paradigmatic shift in which AI-driven evidence synthesis, real-world data mining, and multimodal large language models are redefining what constitutes publishable medical science. This evolution is accompanied by a diversification of publication outputs, as traditional research articles increasingly coexist with algorithmically generated clinical decision aids, dynamic knowledge graphs, and interdisciplinary reports that fuse computational science with clinical inquiry.

Concurrently, the epistemic boundaries of medical publishing are being tested by the tension between AI-enabled acceleration of manuscript preparation and the rigorous evidentiary hierarchies—such as CONSORT, PRISMA, and STARD—that anchor medical credibility. While AI expands the capacity to synthesize heterogeneous biomedical data and democratize access to complex findings, it also introduces novel vulnerabilities: the seamless generation of plausible yet unverified content, the algorithmic amplification of biased or decontextualized evidence, and the erosion of conventional gatekeeping mechanisms. These developments unfold within a high-stakes information ecosystem where inaccuracies do not remain confined to academic discourse but permeate clinical practice and public health, intensifying concerns about infodemics, trust, and the social accountability of medical knowledge. Addressing these intersecting transformations requires moving beyond generic analyses of scholarly communication toward domain-sensitive inquiry into how AI is reshaping the norms, outputs, and governance of medical publication itself.

These developments raise important questions about readability, digital literacy, trust, misinformation, research visibility, and equitable access to scholarly outputs. We welcome original research, reviews, case studies, and conceptual contributions on AI-enabled medical knowledge communication, digital literacy, scholarly publishing, and the societal circulation of medical research. Topics of interest include, but are not limited to the following:

(1) AI4S and scholarly communication;

(2) Knowledge graphs;

(3) Bibliometrics and scientometrics;

(4) Natural language processing and text mining in medical publication analysis;

(5) Digital literacy, trust, and credibility in AI-mediated knowledge environments;

(6) Open access, research visibility, and societal impact of medical publications;

(7) Interdisciplinary perspectives.

Dr. Bowen Song
Dr. Zhinan Wang
Guest Editors

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• artificial intelligence for science (AI4S)
• medical knowledge communication
• digital literacy
• scholarly communication
• knowledge graphs