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.
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
Review Stats
Final search date and database lock: 2026-03-28 17:36:18 CET
Plan: Pro (expanded craft tokens; source: Semantic Scholar)
Source: Semantic Scholar
Total Abstracts/Papers: 5247
Downloaded Abstracts/Papers: 1000
Included original and non-original Abstracts/Papers (all): 311
Included original Abstracts/Papers (Vote counting by direction of effect): 60
Reference Index (links used in paper): 86
Total participants (topic deduplicated ΣN): 1115920
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[135] REGISTERED REPORTS AS A METHOD TO INCREASE CREDIBILITY OF SCIENCE – EXPERIMENTAL STUDY AMONG PSYCHOLOGICAL STUDENTS — https://doi.org/10.15503/jecs20192.67.75
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[166] Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality — https://doi.org/10.2196/37324
[183] Scientific impact increases when researchers publish in open access and international collaboration: A bibliometric analysis on poverty-related disease papers — https://doi.org/10.1371/journal.pone.0203156
[192] ‘AI‐navigating’ or ‘AI‐sinking’? An analysis of verbs in research articles titles suspicious of containing AI‐generated/assisted content — https://doi.org/10.1002/leap.1647
[199] Distance education as a tool to improve researchers’ knowledge on predatory journals in countries with limited resources: the Moroccan experience — https://doi.org/10.1007/s40979-023-00122-7
[225] An Evaluation of Primary Studies Published in Predatory Journals Included in Systematic Reviews From High-Impact Dermatology Journals: Cross-sectional Study — https://doi.org/10.2196/39365
[226] Perspectives From Authors and Editors in the Biomedical Disciplines on Predatory Journals: Survey Study — https://doi.org/10.2196/13769
[239] Visualizing Collaboration Characteristics and Topic Burst on International Mobile Health Research: Bibliometric Analysis — https://doi.org/10.2196/mhealth.9581
[240] Does research through Structured Operational Research and Training (SORT IT) courses impact policy and practice? — https://doi.org/10.5588/pha.15.0062
[296] Arbitrariness of bibliometric parameters: a case study on leading scientists in the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) — https://doi.org/10.1007/s00210-024-03195-4
