Item 1 — Identification as a study of AI methodology, specifying the category of technology used (e.g., deep learning)

Specify the AI techniques used in the study—such as “vision transformers” or “deep learning”—in the article’s title and/or abstract; use judgment regarding the level of specificity.

Explanation and Elaboration: The specific AI techniques employed in a study should be clearly stated in the title or abstract—preferably both. The level of detail should be carefully balanced according to the journal’s target audience and the state of the art in the field, avoiding unnecessary technical complexity while maintaining sufficient specificity to accurately describe the AI techniques used. Striking this balance ensures accessibility without compromising scientific rigor.

Examples
RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning [Title (5), CC-BY 4.0]
To externally validate the performance of three DL algorithms as mammographic screen readers in an independent UK data set. [Abstract (6), CC-BY 4.0]
Textual features were extracted from radiology reports to guide a DL model (ReportGuidedNet) focusing on lesion characteristics. Another DL model (PlainNet) without textual features was developed for comparative analysis. [Abstract (7), CC-BY 4.0]