May 10, 2024, 4:45 a.m. | Siddharth Agarwal, David Wood, Robin Carpenter, Yiran Wei, Marc Modat, Thomas C Booth

cs.CV updates on arXiv.org arxiv.org

arXiv:2405.05647v1 Announce Type: new
Abstract: This letter critically examines the recent article by Infante et al. assessing the utility of large language models (LLMs) like GPT-4, Perplexity, and Bard in identifying urgent findings in emergency radiology reports. While acknowledging the potential of LLMs in generating labels for computer vision, concerns are raised about the ethical implications of using patient data without explicit approval, highlighting the necessity of stringent data protection measures under GDPR.

abstract article arxiv bard chatgpt cs.cv editor emergency ethical ethical considerations gpt gpt-4 language language models large language large language models legal llms perplexity radiology reports type utility while

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