Eyal Zimlichman

Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying

2024 · 108 Zit. · NEJM AI

Evaluating prompt engineering on GPT-3.5’s performance in USMLE-style medical calculations and clinical scenarios generated by GPT-4

2024 · 26 Zit. · Scientific Reports

Comparison of deep learning models for natural language processing-based classification of non-English head CT reports

2020 · 26 Zit. · Neuroradiology

ChatGPT’s adherence to otolaryngology clinical practice guidelines

2024 · 16 Zit. · European Archives of Oto-Rhino-Laryngology

BERT for the Processing of Radiological Reports: An Attention-based Natural Language Processing Algorithm

2021 · 15 Zit. · Academic Radiology

Assessing GPT-3.5 and GPT-4 in Generating International Classification of Diseases Billing Codes

2023 · 11 Zit.

Revolutionizing patient safety with artificial intelligence: the potential of natural language processing and large language models

2023 · 10 Zit. · International Journal for Quality in Health Care

The Limits of Prompt Engineering in Medical Problem-Solving: A Comparative Analysis with ChatGPT on calculation based USMLE Medical Questions

2023 · 9 Zit.

Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections

2023 · 4 Zit. · BJR|Open

Randomized Controlled Trials Evaluating Artificial Intelligence in Cardiovascular Care

2025 · 3 Zit. · JACC Advances

Unlocking the potential of large language models in healthcare: navigating the opportunities and challenges

2024 · 2 Zit. · Future Medicine AI

Evaluating empathy in GPT-4-generated vs. physician-written emergency department discharge letters

2025 · 0 Zit. · Digital Health