Cluster of Excellence: "Machine Learning: New Perspectives for Science", University of Tübingen, Tübingen, Germany.
Hertie Institute for AI in Brain Health & Tübingen AI Center, Tübingen, Germany.
Bioethics. 2024 Jun;38(5):383-390. doi: 10.1111/bioe.13283. Epub 2024 Mar 25.
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.
在基于图像的医学诊断和风险预测模型方面取得了一波突破之后，机器学习（ML）已经成为了一门常规科学。然而，杰出的研究人员声称，由于最近大型语言模型在单一用途应用程序方面取得了惊人的成功，医学 ML 即将发生另一次范式转变——朝着由自然语言驱动的通用模型发展。本文探讨了这一范式转变对伦理辩论的影响。本文将重点关注信任、透明度、患者自主权受到的威胁、临床医生和 ML 模型合作中的责任问题、公平性和隐私性等问题，认为主要问题将与当前的辩论保持一致。然而，由于大型语言模型的运作，所有这些问题的复杂性都增加了。此外，本文还讨论了由于企业利益，大型语言模型的临床评估以及医学中关于大型语言模型的研究的可重复性和可复制性方面所面临的一些深远挑战。
