References
- Chawla DS. What's wrong with the journal impact factor in 5 graphs. nature.com Web site. https://www.nature.com/natureindex/news-blog/whats-wrong-with-the-jif-in-five-graphs. Accessed December 2, 2022
- Zijlstra H, McCullough R. CiteScore: a new metric to help you track journal performance and make decisions. elsevier.com Web site. https://www.elsevier.com/connect/editors-update/citescore-a-new-metric-to-help-you-choose-the-right-journal. Accessed December 2, 2022
- SJR. Journal rankings. scimagojr.com Web site. https://www.scimagojr.com/journalrank.php. Accessed December 2, 2022
- Wikipedia. h-index. en.wikipedia.org Web site. https://en.wikipedia.org/wiki/H-index. Accessed December 2, 2022
- Bluemke DA. Radiology and the impact factor. Radiology 2022;305:247-248 https://doi.org/10.1148/radiol.229016
- Quaderi N. Journal citation reports 2022: COVID-19 research continues to drive increased citation impact. clarivate. com Web site. https://clarivate.com/blog/journal-citationreports-2022-covid-19-research-continues-to-drive-increasedcitation-impact/. Accessed December 2, 2022
- Cell Press. Cell Press journals see impact factor gains in 2022. cell.com Web site. https://www.cell.com/news-do/cell-pressimpact-factors-2022. Accessed December 2, 2022
- Mondal P, Mazur L, Su L, Gope S, Dell E. The upsurge of impact factors in pediatric journals post COVID-19 outbreak: a cross-sectional study. Front Res Metr Anal 2022;7:862537
- Maillard A, Delory T. Blockbuster effect of COVID-19 on the impact factor of infectious disease journals. Clin Microbiol Infect 2022;28:1536-1538 https://doi.org/10.1016/j.cmi.2022.08.011
- Gonzalez-Hermosillo LM, Roldan-Valadez E. Impact factor JUMPS after the 2020 COVID-19 pandemic: a retrospective study in dermatology journals. Ir J Med Sci 2022 Oct 3. [Epub]. https://doi.org/10.1007/s11845-022-03179-4
- Giannos P, Delardas O. The great inflation: how COVID-19 affected the journal impact factor of high impact medical journals. J Med Internet Res 2022 Nov 30. [Epub]. https://doi.org/10.2196/43089
- Garcia-Blanco MD, Valdez-Valdes A, Ternovoy SK, Bueno-Hernandez N, Roldan-Valadez E. Impact factor JUMPS after the 2020 COVID-19 pandemic: a retrospective study in radiology, nuclear medicine & medical imaging journals. Ultrasound Q 2022;38:202-207 https://doi.org/10.1097/RUQ.0000000000000615
- Brandt MD, Ghozy SA, Kallmes DF, McDonald RJ, Kadirvel RD. Comparison of citation rates between Covid-19 and nonCovid-19 articles across 24 major scientific journals. PLoS One 2022;17:e0271071
- Riccaboni M, Verginer L. The impact of the COVID-19 pandemic on scientific research in the life sciences. PLoS One 2022;17:e0263001
- Wikipedia. Impact factor. en.wikipedia.org Web site. https://en.wikipedia.org/wiki/Impact_factor. Accessed December 2, 2022
- Park SH. Guides for the successful conduct and reporting of systematic review and meta-analysis of diagnostic test accuracy studies. Korean J Radiol 2022;23:295-297 https://doi.org/10.3348/kjr.2021.0963
- Park SH, Han K. How to clearly and accurately report odds ratio and hazard ratio in diagnostic research studies? Korean J Radiol 2022;23:777-784 https://doi.org/10.3348/kjr.2022.0249
- Park SH, Han K, Park SY. Mistakes to avoid for accurate and transparent reporting of survival analysis in imaging research. Korean J Radiol 2021;22:1587-1593 https://doi.org/10.3348/kjr.2021.0579
- Atzen SL, Bluemke DA. How to write the perfect abstract for Radiology. Radiology 2022;305:498-501 https://doi.org/10.1148/radiol.229012
- Atzen SL, Bluemke DA. Top 10 tips for writing your scientific Paper: the Radiology scientific style guide. Radiology 2022;304:1-2 https://doi.org/10.1148/radiol.229005
- Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology 2020;294:487-489 https://doi.org/10.1148/radiol.2019192515
- Chang JM, Ha SM. Regional lymphadenopathy following COVID-19 vaccination in patients with or suspicious of breast cancer: a quick summary of current key facts and recommendations. Korean J Radiol 2022;23:691-695 https://doi.org/10.3348/kjr.2022.0292
- Futterer JJ, Nagarajah J. Research highlight: 68Ga-PSMA-11 PET imaging for pelvic nodal metastasis in prostate cancer. Korean J Radiol 2022;23:293-294 https://doi.org/10.3348/kjr.2021.0938
- Goo JM, Jung KW, Kim HY, Kim Y. Potential overdiagnosis with CT lung cancer screening in Taiwanese female: status in South Korea. Korean J Radiol 2022;23:571-573 https://doi.org/10.3348/kjr.2022.0190
- Han K, Jung I. Restricted mean survival time for survival analysis: a quick guide for clinical researchers. Korean J Radiol 2022;23:495-499 https://doi.org/10.3348/kjr.2022.0061
- Kim HC. Role of Yttrium-90 radioembolization for colorectal hepatic metastasis. Korean J Radiol 2022;23:156-158 https://doi.org/10.3348/kjr.2021.0867
- Kwapisz L, Bruining DH, Fletcher JG. Using MR enterography and CT enterography for routine Crohn's surveillance: how we do it now, and how we hope to do it in the future. Korean J Radiol 2022;23:1-5 https://doi.org/10.3348/kjr.2021.0846
- Lee MW, Rhim H. Research highlight: how to use technical and oncologic outcomes of image-guided tumor ablation according to guidelines by Society of Interventional Oncology and DATECAN? Korean J Radiol 2022;23:385-388 https://doi.org/10.3348/kjr.2021.0982
- Lee S, Shin HJ, Kim S, Kim EK. Successful implementation of an artificial intelligence-based computer-aided detection system for chest radiography in daily clinical practice. Korean J Radiol 2022;23:847-852 https://doi.org/10.3348/kjr.2022.0193
- Lee SH, Moon WK. Glandular tissue component on breast ultrasound in dense breasts: a new imaging biomarker for breast cancer risk. Korean J Radiol 2022;23:574-580 https://doi.org/10.3348/kjr.2022.0099
- Park JE, Vollmuth P, Kim N, Kim HS. Research highlight: use of generative images created with artificial intelligence for brain tumor imaging. Korean J Radiol 2022;23:500-504 https://doi.org/10.3348/kjr.2022.0033
- Park SH, Choi JI, Fournier L, Vasey B. Randomized clinical trials of artificial intelligence in medicine: why, when, and how? Korean J Radiol 2022;23:1119-1125 https://doi.org/10.3348/kjr.2022.0834
- Youk JH, Kim EK. Research highlight: artificial intelligence for ruling out negative examinations in screening breast MRI. Korean J Radiol 2022;23:153-155 https://doi.org/10.3348/kjr.2021.0912
- Bluemke DA. The new radiology. Radiology 2019;290:275-276 https://doi.org/10.1148/radiol.2019184023
- Gormly KL. High-resolution T2-weighted MRI to evaluate rectal cancer: why variations matter. Korean J Radiol 2021;22:1475-1480 https://doi.org/10.3348/kjr.2021.0560
- Heo S, Lee SS, Kim SY, Lim YS, Park HJ, Yoon JS, et al. Prediction of decompensation and death in advanced chronic liver disease using deep learning analysis of gadoxetic acid-enhanced MRI. Korean J Radiol 2022;23:1269-1280 https://doi.org/10.3348/kjr.2022.0494
- Kim DW, Ahn H, Kim KW, Lee SS, Kim HJ, Ko Y, et al. Prognostic value of sarcopenia and myosteatosis in patients with resectable pancreatic ductal adenocarcinoma. Korean J Radiol 2022;23:1055-1066 https://doi.org/10.3348/kjr.2022.0277
- Kim YS, Jang MJ, Lee SH, Kim SY, Ha SM, Kwon BR, et al. Use of artificial intelligence for reducing unnecessary recalls at screening mammography: a simulation study. Korean J Radiol 2022;23:1241-1250 https://doi.org/10.3348/kjr.2022.0263
- Lee JH, Kim KH, Lee EH, Ahn JS, Ryu JK, Park YM, et al. Improving the performance of radiologists using artificial intelligence-based detection support software for mammography: a multi-reader study. Korean J Radiol 2022;23:505-516 https://doi.org/10.3348/kjr.2021.0476
- Park HJ, Yoon JS, Lee SS, Suk HI, Park B, Sung YS, et al. Deep learning-based assessment of functional liver capacity using gadoxetic acid-enhanced hepatobiliary phase MRI. Korean J Radiol 2022;23:720-731 https://doi.org/10.3348/kjr.2021.0892
- Yoo H, Kim EY, Kim H, Choi YR, Kim MY, Hwang SH, et al. Artificial intelligence-based identification of normal chest radiographs: a simulation study in a multicenter health screening cohort. Korean J Radiol 2022;23:1009-1018 https://doi.org/10.3348/kjr.2022.0189
- Park SH, Han K, Jang HY, Park JE, Lee JG, Kim DW, et al. Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis. Radiology 2022 Nov 8. [Epub]. https://doi.org/10.1148/radiol.220182
- Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2020;2:e200029
- Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ 2020;370:m3164
- Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRITAI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ 2020;370:m3210
- Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022;377:e070904
- Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 2021;11:e048008
- Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021;11:e047709