T. Subramanian is a resident physician, University of Chicago Medical Centera fellow, MacLean Center for Clinical Medical Ethics, Chicago, Illinois.
L. Pocivavsek is assistant professor of surgery, Section of Vascular Surgery, Department of Surgery, University of Chicago Medicine, Chicago, Illinois.
Acad Med. 2023 Jun 1;98(6S):S34-S36. doi: 10.1097/ACM.0000000000005189. Epub 2023 May 23.
Advances in high-resolution, cross-sectional imaging have changed the practice of medicine. These innovations have clearly benefited patient care yet have also led to a decreased dependence on the art of medicine, with its emphasis on obtaining a thoughtful history and thorough physical examination to elicit the same diagnosis that imaging provides. What remains to be determined is how physicians can balance these technological advances with their own ability to use clinical experience and judgment. This can be seen not only with the use of high-level imaging but also with the increasing use of machine-learning models throughout medicine. The authors contend that these should be seen not as a replacement for the physician, but as another tool in their arsenal in determining management decisions. These issues are salient for surgeons, who, given the serious undertaking required to operate on a person, must develop trust-based relationship with their patients. Navigating this new field brings with it several ethical conundrums that must be addressed, with the final goal being to provide optimal patient care without sacrificing the human element involved, from either the physician or the patient. The authors examine these less-than-simple challenges, which will continue to develop as physicians use the increasing amount of machine-based knowledge available to them.
高分辨率、横截面成像技术的进步改变了医学实践。这些创新显然使患者受益，但也导致人们对医学艺术的依赖程度降低，而医学艺术强调获取深思熟虑的病史和彻底的体格检查，以得出与影像学相同的诊断。仍有待确定的是，医生如何在利用自己的临床经验和判断力的同时平衡这些技术进步。这不仅体现在使用高级成像技术上，也体现在整个医学领域越来越多地使用机器学习模型上。作者认为，这些模型不应被视为替代医生，而应被视为医生在确定管理决策时的另一种工具。这些问题对外科医生来说非常重要，因为外科医生要对一个人进行手术，这是一项严肃的任务，他们必须与患者建立基于信任的关系。在这个新领域中，必须解决几个伦理难题，最终目标是在不牺牲医患双方的人文因素的情况下，为患者提供最佳的医疗护理。作者研究了这些不简单的挑战，随着医生使用越来越多的基于机器的知识，这些挑战将继续发展。
