von Ulmenstein Ulrich, Tretter Max, Ehrlich David B, Lauppert von Peharnik Christina
Chair of Public Law, Justus Liebig University of Giessen, Giessen, Germany.
Department of Systematic Theology, Friedrich Alexander University of Erlangen Nuremberg, Erlangen, Bavaria, Germany.
Front Artif Intell. 2022 Aug 1;5:913093. doi: 10.3389/frai.2022.913093. eCollection 2022.
Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.
当前的技术和医学进步为实现新的医学确定性的努力提供了巨大动力。人工智能能够在预测个人健康状况方面实现前所未有的精准度和能力。但是，正如我们所阐述的，这种获取医学信息的新方式有可能加剧健康保险市场中的逆向选择。我们进行了一项跨学科概念分析，从法律、伦理和经济角度研究如何避免这种风险。我们探讨禁止或限制人工智能及其医学应用以及限制医学确定性是否可行且有效，结果发现基于这些限制的方法都不能提供完全充分的解决方案。因此，我们认为在未来关于人工智能预测医学应用的讨论中绝不能忽视这一挑战，应在结构层面加以应对，并且我们鼓励对此主题进行进一步研究。
