Explainable artificial intelligence in medical ultrasound: a WoSCC-based bibliometric analysis, evidence map, and Scopus concordance assessment
Abstract
Background: Explainable artificial intelligence (XAI) is now used across medical ultrasound, yet it remains unclear whether the field has moved beyond producing explanation displays. This review examined whether the recent expansion of ultrasound XAI has been matched by explanation-specific evaluation and clinically relevant validation, and whether the leading bibliometric patterns are consistent across databases.
Methods: Web of Science Core Collection (WoSCC) and Scopus were searched for records published from 2010 to 2026 and indexed through July 9, 2026. After cross-database deduplication, 1,745 unique records were assessed and database-specific Broad and nested Core XAI datasets were constructed. WoSCC supported bibliometric mapping and structured evidence coding; Scopus and union datasets were used to assess incremental coverage and cross-database concordance.
Results: The searches retrieved 2,706 records and yielded 640 Union Broad and 514 Union Core XAI records; the primary WoSCC datasets contained 469 and 387 records, respectively. Publications rose sharply after 2023, with 2026 representing an incomplete year. SHAP (186/387, 48.1%) and CAM/Grad-CAM (79/387, 20.4%) predominated. An explanation output was reported or displayed without an identified evaluation procedure in 280/387 studies (72.4%), while no explicit explanation-evaluation information was identified in the reviewed evidence sources for 90/387 studies (23.3%); only 17 (4.4%) reported expert/reader,…
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