SAIMSARA Journal

Machine-Readable Science • ISSN 3054-3991

Future of Scientific Publishing: Scoping Review with ☸️SAIMSARA.

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Digital Health & Biotech

Issue 3, Volume 1, 2026

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DOI: 10.62487/saimsarada26548b

Editorial note
• Last update: 2026-03-28 22:45:05
What is this paper about
This paper shows that the future of scientific publishing will not be defined by AI alone, but by whether publishing can become more transparent, machine-readable, open, and resistant to predatory and low-signal science at the same time. The full read is worth it because it maps where this transition is already happening, where it is failing, and which concrete changes in publishing, peer review, and research evaluation are most likely to matter next.
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Abstract: To synthesize contemporary research on the future of scientific publishing, focusing on the integration of artificial intelligence, the transition to open access, the mitigation of predatory practices, and the evolution of structural reporting standards. The review utilises 86 studies. Across the mapped evidence, the most prominent signal is that AI will increasingly shape scholarly communication, with 79.0% of corresponding authors in top medical journals anticipating a major future role for AI in the research lifecycle, while current evaluations indicate AI outputs still require expert oversight rather than autonomous deployment. In parallel, the literature indicates mounting system-level strain from information overload and attention concentration, including power-law declines in citation probability amid exponential growth and “paperdemic” dynamics that amplify low-signal publication streams. Open access expansion is consistently positioned as a central trajectory, yet persistent cost barriers and slow discipline-level transitions (e.g., a 77-year projection to full OA in communication research) highlight that access reform is as much economic and governance-related as it is technical. Practical implications are clearest for clinical and health researchers: strengthening publishing literacy and screening for predatory outlets is necessary to protect evidence synthesis and downstream decision-making. Future research should prioritize evaluative, field-stratified studies of AI governance and machine-readable publishing (including disclosure, detection, and structured reporting) that measure impacts on integrity, reproducibility, and equity under real editorial and funding constraints.

Keywords: Large Language Models; Open Access; Scientific Misconduct; Automated Article Generation; Predatory Publishing; Scholarly Communication; Research Reproducibility; Registered Reports; AI-Generated Text Detection; Publication Ethics

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