# Artificial intelligence-aided assignment of journal submissions to associate editors—a feasibility study on IEEE transactions on medical imaging

## TL;DR

This paper explores using AI to match journal submissions with suitable editors, improving efficiency and review quality in medical imaging journals.

## Contribution

The study introduces a novel AI-based approach combining keyword and textual similarity for assigning journal submissions to editors.

## Key findings

- Keyword similarity is the dominant factor in matching performance.
- Textual similarity provides complementary gains when PCA is applied.
- Structured keywords alone offer strong matching accuracy with minimal need for abstracts.

## Abstract

Efficient and accurate assignment of journal submissions to suitable associate editors (AEs) is critical in maintaining review quality and timeliness, particularly in high-volume, rapidly evolving fields such as medical imaging. This study investigates the feasibility of leveraging large language models for AE-paper matching in IEEE Transactions on Medical Imaging. An AE database was curated from historical AE assignments and AE-authored publications, and extracted six key textual components from each paper title, four categories of structured keywords, and abstracts. ModernBERT was employed locally to generate high-dimensional semantic embeddings, which were then reduced using principal component analysis (PCA) for efficient similarity computation. Keyword similarity, derived from structured domain-specific metadata, and textual similarity from ModernBERT embeddings were combined to rank the candidate AEs. Experiments on internal (historical assignments) and external (AE Publications) test sets showed that keyword similarity is the dominant contributor to matching performance. Contrarily, textual similarity offers complementary gains, particularly when PCA is applied. Ablation studies confirmed that structured keywords alone provide strong matching accuracy, with titles offering additional benefits and abstracts offering minimal improvements. The proposed approach offers a practical, interpretable, and scalable tool for editorial workflows, reduces manual workload, and supports high-quality peer reviews.