After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single‐purpose applications toward generalist models, driven by natural language. This article investigates the implications of this paradigm shift for the ethical debate. Focusing on issues like trust, transparency, threats of patient autonomy, responsibility issues in the collaboration of clinicians and ML models, fairness, and privacy, it will be argued that the main problems will be continuous with the current debate. However, due to functioning of large language models, the complexity of all these problems increases. In addition, the article discusses some profound challenges for the clinical evaluation of large language models and threats to the reproducibility and replicability of studies about large language models in medicine due to corporate interests.

中文翻译：

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范式转变？——论医学大语言模型的伦理

在基于图像的医疗诊断和风险预测模型取得一波突破之后，机器学习（ML）已成为一门常规科学。然而，著名研究人员声称，由于大型语言模型最近取得了惊人的成功，医学机器学习的另一个范式转变即将到来，从单一用途应用程序转向由自然语言驱动的通用模型。本文研究了这种范式转变对伦理辩论的影响。重点关注信任、透明度、患者自主权的威胁、临床医生和机器学习模型合作中的责任问题、公平性和隐私等问题，有人认为，主要问题将继续当前的辩论。然而，由于大型语言模型的运行，所有这些问题的复杂性都增加了。此外，文章还讨论了大语言模型临床评估面临的一些深刻挑战，以及由于企业利益而对医学大语言模型研究的可重复性和可复制性造成的威胁。