L’intelligence artificielle générative peut-elle améliorer le choix des revues scientifiques? Évaluation des approches traditionnelles et algorithmiques en recherche médicale
DOI :
https://doi.org/10.62438/tunismed.v103i11.6265Mots-clés :
Biais algorithmique, Bibliométrie, Diffusion de la recherche, Éthique éditoriale, Facteur d’impact, Normes de publication, Recherche d’information, Scimago, Scopus, Validation logicielle, Web of ScienceRésumé
Introduction: Le choix du journal constitue une étape déterminante du processus de publication scientifique, influençant la visibilité, l’impact et la crédibilité des travaux publiés. Cette tâche s’est complexifiée avec la multiplication des revues, l’émergence de pratiques prédatrices et la diversité des critères éditoriaux. Cette revue narrative propose un aperçu des outils classiques, des plateformes fondées sur l’intelligence artificielle (IA) et des modèles génératifs (Chat Generative Pre-trained Transformer (ChatGPT), Grok) utilisés pour recommander des revues adaptées à un manuscrit non publié.
Méthodes: Six outils ont été testés (Springer Journal Finder, Jane, Manuscript Matcher, Trinka Journal Finder, ChatGPT et Grok) à partir du résumé ou du texte intégral d’un article clinique sur les lombalgies non spécifiques. Les résultats ont été comparés selon la pertinence thématique, la disponibilité d’indicateurs bibliométriques et la transparence des recommandations.
Résultats: Les outils classiques sont limités par la restriction de leur couverture éditoriale et l’absence d’indicateurs clés. Les plateformes d’IA offrent une couverture plus large mais manquent parfois de précision sur des sujets ciblés. Les modèles génératifs se distinguent par leur capacité à structurer les recommandations, bien que les données fournies (facteur d’impact, frais, délais) soient souvent inexactes ou invérifiables. Plusieurs biais technologiques et limites algorithmiques affectent la fiabilité globale de ces systèmes.
Conclusion: Bien que les outils d’IA accélèrent l’identification initiale des revues, ils suggèrent fréquemment des titres hors champ et des métriques erronées. Leur intérêt réside dans une exploration préliminaire, l’utilisateur devant conserver un rôle central, combinant assistance computationnelle et jugement scientifique pour un choix éclairé.
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