Future and Scientific Publishing: Systematic Review with ☸️SAIMSARA.



saimsara.com

Review Stats
Identification of studies via EPMC (titles/abstracts) Identification Screening Included Records identified:n=494Records excluded:n=0 Records assessed for eligibilityn=494Records excluded:n=191 Studies included in reviewn=303 PRISMA Diagram generated by ☸️ SAIMSARA
⛛OSMA Triangle Effect-of Predictor → Outcome scientific publishing  →  future Beneficial for patients ΣN=7417 (1%) Harmful for patients ΣN=1274 (0%) Neutral ΣN=1169709 (99%) 0 ⛛OSMA Triangle generated by ☸️SAIMSARA
Outcome-Sentiment Meta-Analysis (OSMA): (LLM-only)
Frame: Effect-of Predictor → Outcome • Source: Europe PMC
Outcome: future Typical timepoints: 981-day, 3-y. Reported metrics: %, CI, p.
Common endpoints: Common endpoints: readmission, complications.
Predictor: scientific publishing — exposure/predictor. Typical comparator: control, opinion articles, other articles, overseas meetings. future.




1. Introduction
The landscape of scientific publishing is undergoing rapid transformation, driven by technological advancements, evolving research methodologies, and changing scholarly communication paradigms. This review synthesizes current research on the future of scientific publishing, exploring emerging trends, challenges, and opportunities across various disciplines.

2. Aim
To systematically review and synthesize findings from original studies concerning the future of scientific publishing, identifying key themes, challenges, and emerging trends.

3. Methods
3.1 Eligibility criteria: This review exclusively includes original studies extracted from the provided structured summary. Editorials, conference papers, and reviews were excluded.
3.1 Study selection: Studies were selected based on their relevance to the query "future and scientific publishing," as determined by the pre-defined session keyword gate.
3.2 Risk of bias: Risk of bias was inferred qualitatively based on the study design information available in the structured summary. Studies with "N/A" for study design were considered to have limited information on methodological rigor. The lack of specified directionality in most studies also presents a limitation.
3.3 Synthesis: The synthesis was performed using an autonomous multilayer AI research agent, employing keyword normalization, retrieval, and structuring, followed by paper synthesis, as detailed in the SAIMSARA About section.

4. Results
4.1 Study characteristics: The included studies span a wide range of disciplines and publication years, with a significant number published in 2024 and 2025, indicating current research focus. Study designs are predominantly mixed, with some cohort and cross-sectional studies. Populations and settings vary widely, from comparative physiology and medical journal editors to early career scientists and specific research fields. Follow-up periods are often not specified or are historical.

4.2 Main numerical result aligned to the query:
The adoption of Artificial Intelligence (AI) in scientific publishing is a significant emerging trend. A survey of 59 medical journal editors worldwide indicated that 49% of journals already use AI tools, with 76% for plagiarism detection and 35% for data verification [2]. Looking ahead, 81% of these editors anticipate a major role for AI in publishing within 10 years, citing time savings (79%) and cost reduction (43%) as key benefits, though 71% expressed concerns about bias and 60% about lack of accountability [2]. Similarly, 79% of global clinical researchers are aware of Large Language Models (LLMs), with 18.7% having used LLMs in publications, and 58.1% believing journals should allow AI use, provided regulations are in place [49].

4.3 Topic synthesis:


5. Discussion
5.1 Principal finding: The integration of AI and LLMs into scientific publishing is rapidly advancing, with a strong expectation of their significant role within the next decade [2, 49]. While AI offers benefits like time savings and cost reduction, critical concerns regarding bias, accountability, and ethical guidelines persist [2, 19, 21, 49].

5.2 Clinical implications:


5.3 Research implications / key gaps:


5.4 Limitations:


5.5 Future directions:


6. Conclusion
The integration of AI and LLMs into scientific publishing is a significant and rapidly evolving trend, with editors anticipating a major role for these technologies within the next decade, driven by potential time savings and cost reductions [2, 49]. However, this integration is accompanied by critical concerns regarding bias, accountability, and the need for robust ethical guidelines [2, 19, 21, 49]. The most significant limitation to a definitive quantitative synthesis is the heterogeneity of metrics and the qualitative nature of many findings. Future research should focus on developing comprehensive AI governance frameworks and empirically validating AI tools to ensure their responsible and effective integration into scientific publishing [22, 49, 123, 129].

References
SAIMSARA Session Index — session.json

Figure 1. Publication-year distribution of included originals
Figure 1. Publication-year distribution of included originals

Figure 2. Study-design distribution of included originals
Figure 2. Study-design distribution

Figure 3. Study-type (directionality) distribution of included originals
Figure 3. Directionality distribution

Figure 4. Main extracted research topics
Figure 4. Main extracted research topics (Results)

Figure 5. Limitations of current studies (topics)
Figure 5. Limitations of current studies (topics)

Figure 6. Future research directions (topics)
Figure 6. Future research directions (topics)