5 citations · 13 across the 9 of their papers we have counts for
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A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models
Andrei Marian Feier, Veysel Kocaman, Yigit Gul +6
Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or ethically complex conditions c…
Specialty-Specific Medical Language Model for Immune-Mediated Diseases
Veysel Kocaman, Gursev Pirge, Yigit Gul +3
Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Terminology for immune-mediated and…
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?
Veysel Kocaman, Muhammed Santas, Yigit Gul +2
We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and Jo…
Beyond Negation Detection: Comprehensive Assertion Detection Models for Clinical NLP
Veysel Kocaman, Yigit Gul, M. Aytug Kaya +4
Assertion status detection is a critical yet often overlooked component of clinical NLP, essential for accurately attributing extracted medical facts. Past studies have narrowly fo…
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets
Veysel Kocaman, Hasham Ul Haq, David Talby
Recent research advances achieve human-level accuracy for de-identifying free-text clinical notes on research datasets, but gaps remain in reproducing this in large real-world sett…
Understanding COVID-19 News Coverage using Medical NLP
Ali Emre Varol, Veysel Kocaman, Hasham Ul Haq +1
Being a global pandemic, the COVID-19 outbreak received global media attention. In this study, we analyze news publications from CNN and The Guardian - two of the world's most infl…