Title: Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results f...

Authors: Dong Wook Kim, Hye Young Jang, Kyung Won Kim, Youngbin Shin, Seong Ho Park

Publishing date: 01-Jan-2019

Article Expected CPY (Citations per Year): 3.74 Expected CPY Help

Article Actual CPY: 65.20 Actual CPY Help

Article Co-Citation FCR (Field Citation Rate): 4.51 FCR Help

Article Co-Citation Network Size: 17020 Co-Citation Network Size Help

Article Topics: Artificial Intelligence in Medicine, Radiomics in Medical Imaging Analysis, Deep Learning in Medical Image Analysis Topics help

Article Keywords: Medical Image Analysis, Artificial Intelligences, Medical Imaging, Texture Analysis Keywords Help

Journal: Korean journal of radiology/Korean Journal of Radiology

Journal IF-ycr: 3.319 Journal IF-ycr Help

Journal short code: NA

Journal ISSN: 1229-6929

Journal OA-ID: 75759149

Expected CPY Help: Predicted citations per year for this article, derived from its Field Citation Rate (FCR) using a benchmark regression of NIH-funded papers. Values above actual CPY indicate under-performance; below indicate over-performance. Used as the denominator of the Relative Citation Ratio.

Actual CPY Help: Average yearly citations the article has received from publication through the current year, adjusted for partial years. Used as the numerator of the Relative Citation Ratio.

FCR Help: Mean journal citation rate for all papers in the article's co-citation network. For each network paper we substitute its journal’s impact factor (calculated from open data) as a proxy for citations per year, then average these values. This captures the citation intensity of the article's immediate research field and forms the basis for computing expected CPY.

Co-Citation Network Size Help: Number of unique papers co-cited with this article by its citing papers; larger networks yield more stable field estimates when calculating FCR and expected CPY.

Topics Help: OpenAlex assigns topics to each paper with an AI model that considers the title, abstract, journal, and citation links. Tags are chosen from about 4,500 research areas, and the highest-confidence tag becomes the paper's primary topic. Every topic sits in a hierarchy of domain, field, and subfield, so you can see exactly where the work fits in the wider map of science.

Keywords Help: Keywords are generated automatically from the paper's assigned topics. The OpenAlex system selects candidate terms, then keeps up to five that match closely with the title or abstract. These keywords highlight specific concepts or methods and give a quick complement to the broader topic tags.

Journal IF-ycr Help: Journal IF-ycr is a two-year impact factor recalculated from OpenAlex's open citation data. For a given journal and year Y, we:

1. Count citations made in year Y by any paper to items that the journal published in years Y-1 and Y-2 (excluding the current year).
2. Divide that citation count by the number of articles the journal published in those same two years.