Publication Trends in Artificial Intelligence and Explainable Artificial Intelligence for Medical Image Analysis: An OpenAlex Bibliometric Dataset (2020–2025)

This dataset contains bibliometric records retrieved from the OpenAlex scholarly database for publications related to Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) in medical image analysis published between 2020 and 2025. Publications were identified using predefined keyword-based search strategies covering AI methodologies. The AI query combined the terms: (artificial intelligence, machine learning, deep learning, convolutional neural network (CNN), transformer, vision transformer, foundation model, vision-language model, and large language model). The XAI query combined: (explainable artificial intelligence, explainable AI, explainability, interpretability, interpretable, XAI, Grad-CAM, SHAP, LIME, saliency, attention map, and class activation map). Each query was combined with medical imaging terms (medical imaging, radiology, medical image analysis, MRI, CT, PET, ultrasound, mammography, chest X-ray, pathology, and histopathology) using the Boolean expression (AI/XAI terms) AND (medical imaging terms). Duplicate records were removed using unique OpenAlex identifiers. After duplicate records were removed, a total of 67,116 and 7,059 publications were retained and analyzed for AI and XAI publications, respectively. The dataset includes annual publication counts and processed outputs used to evaluate publication trends and the relative growth of explainable AI research within medical image analysis. This repository also includes the Python source code used to query the OpenAlex API, retrieve publication records, perform deduplication, generate annual publication statistics, and create all figures presented in the accompanying analysis. The scripts are provided to ensure full reproducibility and reuse of the bibliometric workflow. An OpenAlex API Key is required.