Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Would Uro_Chat, a Newly Developed Generative Artificial Intelligence Large Language Model, Have Successfully Passed the In-Service Assessment Questions of the European Board of Urology in 2022?
Matthias May, Katharina Körner-Riffard, Martin Marszalek, et al.
European Urology Oncology (2023) Vol. 7, Iss. 1, pp. 155-156
Closed Access | Times Cited: 8

Showing 8 citing articles:

How does artificial intelligence master urological board examinations? A comparative analysis of different Large Language Models’ accuracy and reliability in the 2022 In-Service Assessment of the European Board of Urology
Lisa Kollitsch, Klaus Eredics, Martin Marszalek, et al.
World Journal of Urology (2024) Vol. 42, Iss. 1
Closed Access | Times Cited: 18
Superhuman performance on urology board questions using an explainable language model enhanced with European Association of Urology guidelines
Martin J. Hetz, Nicolas Carl, Sarah Haggenmüller, et al.
ESMO Real World Data and Digital Oncology (2024) Vol. 6, pp. 100078-100078
Open Access | Times Cited: 7
Which current chatbot is more competent in urological theoretical knowledge? A comparative analysis by the European board of urology in-service assessment
Mehmet Fatih Şahin, Çağrı Doğan, Erdem Can Topkaç, et al.
World Journal of Urology (2025) Vol. 43, Iss. 1
Open Access
Large Language Models in Uro-oncology
Dyke Ferber, Jakob Nikolas Kather
European Urology Oncology (2023) Vol. 7, Iss. 1, pp. 157-159
Closed Access | Times Cited: 11
Evaluating the Efficacy of AI Chatbots as Tutors in Urology: A Comparative Analysis of Responses to the 2022 In-Service Assessment of the European Board of Urology
Matthias May, Katharina Körner-Riffard, Lisa Kollitsch, et al.
Urologia Internationalis (2024) Vol. 108, Iss. 4, pp. 359-366
Open Access | Times Cited: 3
Exploring Large Language Models and the Metaverse for Urologic Applications: Potential, Challenges, and the Path Forward
Hyung Jun Park, Eun Joung Kim, Jungyoon Kim
International Neurourology Journal (2024) Vol. 28, Iss. Suppl 2, pp. S65-73
Open Access | Times Cited: 1
Can ChatGPT Realistically and Reproducibly Assess the Difficulty Level of Written Questions in the In-Service Assessment of the European Board of Urology? [Urology, 2023 Jul;177:29-33. doi: 10.1016/j.urology.2023.05.010]
Matthias May, Katharina Körner-Riffard, Lisa Kollitsch
Urology (2023) Vol. 183, pp. 302-303
Closed Access | Times Cited: 2
Beyond the hype: Unveiling the challenges of large language models in urology
Jethro C.C. Kwong, David‐Dan Nguyen, Adree Khondker, et al.
Canadian Urological Association Journal (2024) Vol. 18, Iss. 10, pp. 333-4
Open Access
Page 1

Scroll to top