Can generative AI improve academic journal selection decisions? Assessing traditional and algorithmic approaches in medical research

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Published Jan 22, 2026
https://doi.org/10.62438/tunismed.v103i11.6265
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Vol. 103 No. 11 (2025): Tunis Med Nov 2025

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Jabeur Methnani
Research laboratory "LR12SP11", Biochemistry Department, Sahloul University Hospital, Sousse, Tunisia
Meriem Gaddas
Research Laboratory LR12SP09 'Heart Failure' , Service of Physiology and Functional Explorations, Farhat Hached University Hospital, Faculty of Medicine 'Ibn el Jazzar' of Sousse, University of Sousse, Tunisia
Houssem Thabet
Research Laboratory LR12SP09 'Heart Failure' Sousse, Farhat Hached University Hospital, Cardiology Department, Sahloul University hospital, Faculty of Medicine 'Ibn el Jazzar' of Sousse,University of Sousse,Tunisia
Ismail Dergaa
High Institute of Sport and Physical Education of Ksar Said, University of Manouba, Manouba, Tunisia
Helmi Ben Saad
Research Laboratory LR12SP09 'Heart Failure' , Service of Physiology and Functional Explorations, Farhat Hached University Hospital, Faculty of Medicine 'Ibn el Jazzar' of Sousse, University of Sousse, Tunisia

Abstract

Introduction: Journal selection is a critical step in the scientific publishing process, influencing the visibility, impact, and credibility of the published work. This task has become increasingly complex due to the proliferation of journals, predatory practices, and the diversity of editorial criteria. This narrative review presented an overview of classical tools, artificial intelligence (AI)-driven platforms, and generative models (ChatGPT, Grok) used to recommend suitable journals for an unpublished manuscript.


Methods: Six tools were tested (Springer Journal Finder, Jane, Manuscript Matcher, Trinka Journal Finder, ChatGPT, and Grok) using either the abstract or full text of a clinical article on nonspecific low back pain. The results were compared based on thematic relevance, availability of bibliometric indicators, and transparency of the recommendations.


Results: Classical tools are limited by their narrow editorial scope and the absence of key indicators. AI platforms offer broader coverage but sometimes lack precision for targeted topics. Generative tools stand out for their ability to structure recommendations, although the data provided (impact factor, fees, timelines) are often inaccurate or unverifiable. Several technological biases and algorithmic limitations impact the overall reliability of these systems.


Conclusion: While AI tools expedite initial journal identification, they frequently suggest journals outside the manuscript's scope and provide incorrect journal metrics. These systems function best as exploratory instruments rather than authoritative advisors. The most successful approach positions the researcher as the primary decision-maker who employs computational assistance to survey options while exercising scholarly judgment for final determinations.

Keywords:

Algorithmic Bias, Bibliometrics, Editorial Ethics, Impact Factor, Information Retrieval, Publication Standards, Research Dissemination, Scopus, Scimago, Software Validation, Web of Science

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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