OpenAlex Citation Counts

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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:

Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study
Masao Noda, Takayoshi Ueno, Ryota Koshu, et al.
JMIR Medical Education (2024) Vol. 10, pp. e57054-e57054
Open Access | Times Cited: 15

Showing 15 citing articles:

Custom GPTs Enhancing Performance and Evidence Compared with GPT-3.5, GPT-4, and GPT-4o? A Study on the Emergency Medicine Specialist Examination
C F Liu, Chien‐Ta Bruce Ho, Tzu-Chi Wu
Healthcare (2024) Vol. 12, Iss. 17, pp. 1726-1726
Open Access | Times Cited: 6

From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance
Markus Kipp
Information (2024) Vol. 15, Iss. 9, pp. 543-543
Open Access | Times Cited: 4

Evaluating AI Proficiency in Nuclear Cardiology: Large Language Models take on the Board Preparation Exam
Valerie Builoff, Aakash Shanbhag, Robert J.H. Miller, et al.
Journal of Nuclear Cardiology (2024), pp. 102089-102089
Closed Access | Times Cited: 4

Comparative Performance Evaluation of Multimodal Large Language Models, Radiologist, and Anatomist in Visual Neuroanatomy Questions
Yasin Celal Güneş, Mehmet Ülkir
Uludağ Üniversitesi Tıp Fakültesi Dergisi (2025) Vol. 50, Iss. 3, pp. 551-556
Open Access

Digitalomics: Towards Artificial Intelligence / Machine Learning-Based Precision Cardiovascular Medicine
Akihiro Nomura, Yasuaki Takeji, Masaya Shimojima, et al.
Circulation Journal (2025)
Open Access

Assessing unknown potential—quality and limitations of different large language models in the field of otorhinolaryngology
Christoph Raphael Buhr, Harry A. Smith, Tilman Huppertz, et al.
Acta Oto-Laryngologica (2024) Vol. 144, Iss. 3, pp. 237-242
Open Access | Times Cited: 3

Evaluating AI Proficiency in Nuclear Cardiology: Large Language Models take on the Board Preparation Exam
Valerie Builoff, Aakash Shanbhag, Robert JH Miller, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3

Challenging ChatGPT4V for the Diagnosis of Oral Diseases and Conditions
Márcio Diniz Freitas, Lucía Lago‐Méndez, Jacobo Limeres Posse, et al.
Oral Diseases (2024)
Closed Access | Times Cited: 3

Reforming China's Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era (Preprint)
Wenting Tong, Xiao‐Wen Zhang, Haiping Zeng, et al.
JMIR Medical Education (2024) Vol. 10, pp. e48594-e48594
Open Access | Times Cited: 1

Evaluating ChatGPT's effectiveness and tendencies in Japanese internal medicine
Yudai Kaneda, Akari Tayuinosho, Rika Tomoyose, et al.
Journal of Evaluation in Clinical Practice (2024) Vol. 30, Iss. 6, pp. 1017-1023
Closed Access

Comparative Assessment of Otolaryngology Knowledge Among Large Language Models
Dante J. Merlino, Santiago Romero‐Brufau, George Saieed, et al.
The Laryngoscope (2024) Vol. 135, Iss. 2, pp. 629-634
Closed Access

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