SynthesisFrontiers in oncology2026
Artificial intelligence in oncology publishing: a systematic review and policy analysis of high-impact journals.
Synthesis in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Generative artificial intelligence (AI) is reshaping scholarly communication, yet guidance for its responsible use remains uneven across biomedical journals. We aimed to systematically assess editorial policies governing AI-assisted writing in high-impact oncology journals. Methods: We conducted a systematic review of publicly available editorial and normative documents, operationalized as a cross-sectional policy audit. Oncology journals with a 2023 Journal Impact Factor ≥5 (JCR 2024) were included. Author instructions, editorial policies, and publisher statements issued between January 2020 and March 2025 were analyzed across four domains: authorship, disclosure, permissible uses, and enforcement. Results: Sixty journals met inclusion criteria. Most journals prohibit AI systems as authors (58/60, 96.7%), reaffirming human accountability. Disclosure of AI use is mandated by 58/60 journals (96.7%), although reporting requirements vary in placement and specificity. Permissible uses are recognized by 58/60 journals (96.7%), generally limited to language editing and formatting under human supervision, while autonomous content generation or interpretation is discouraged. Enforcement provisions are present in 21/60 journals (35.0%), indicating incomplete standardization. At publisher level, disclosure adoption is universal in Elsevier (17/17), Springer Nature (20/20), AACR (6/6), Wiley (6/6), and AMA (1/1), and present in 8/10 journals in the "Other" category. Enforcement varies widely across publishers. Discussion: Editorial policies show strong convergence on core principles but remain heterogeneous in implementation, particularly regarding enforcement. We propose a cross-publisher "AI Policy Minimum Dataset" including standardized disclosures, defined permissible uses, and proportionate enforcement mechanisms, supported by transparent and regularly updated policy frameworks. Greater harmonization is essential to ensure integrity, accountability, and equitable use of AI in oncology publishing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.