Department of Internal Medicine, Icahn School of Medicine at Mount Sinai St' Luke and Mount Sinai West, New York, NY, United States.
Eur J Intern Med. 2018 Feb;48:e13-e14. doi: 10.1016/j.ejim.2017.06.017. Epub 2017 Jun 23.
Physicians in everyday clinical practice are under pressure to innovate faster than ever because of the rapid, exponential growth in healthcare data. "Big data" refers to extremely large data sets that cannot be analyzed or interpreted using traditional data processing methods. In fact, big data itself is meaningless, but processing it offers the promise of unlocking novel insights and accelerating breakthroughs in medicine-which in turn has the potential to transform current clinical practice. Physicians can analyze big data, but at present it requires a large amount of time and sophisticated analytic tools such as supercomputers. However, the rise of artificial intelligence (AI) in the era of big data could assist physicians in shortening processing times and improving the quality of patient care in clinical practice. This editorial provides a glimpse at the potential uses of AI technology in clinical practice and considers the possibility of AI replacing physicians, perhaps altogether. Physicians diagnose diseases based on personal medical histories, individual biomarkers, simple scores (e.g., CURB-65, MELD), and their physical examinations of individual patients. In contrast, AI can diagnose diseases based on a complex algorithm using hundreds of biomarkers, imaging results from millions of patients, aggregated published clinical research from PubMed, and thousands of physician's notes from electronic health records (EHRs). While AI could assist physicians in many ways, it is unlikely to replace physicians in the foreseeable future. Let us look at the emerging uses of AI in medicine.
在日常临床实践中,由于医疗保健数据的快速、指数级增长,医生面临着比以往任何时候都更快创新的压力。“大数据”是指使用传统数据处理方法无法分析或解释的超大数据集。事实上,大数据本身并没有意义,但处理它有望揭示新的见解,并加速医学突破,从而有可能改变当前的临床实践。医生可以分析大数据,但目前这需要大量的时间和复杂的分析工具,如超级计算机。然而,大数据时代人工智能(AI)的兴起可以帮助医生缩短处理时间,并提高临床实践中患者护理的质量。这篇社论提供了对人工智能技术在临床实践中潜在用途的一瞥,并考虑了人工智能取代医生的可能性,也许是完全取代。医生根据个人病史、个体生物标志物、简单评分(例如,CURB-65、MELD)以及对个体患者的体格检查来诊断疾病。相比之下,人工智能可以使用数百个生物标志物、来自数百万患者的成像结果、从 PubMed 聚合的已发表临床研究以及来自电子健康记录 (EHR) 的数千名医生的笔记,基于复杂算法来诊断疾病。虽然人工智能可以在许多方面帮助医生,但在可预见的未来,它不太可能取代医生。让我们来看看人工智能在医学中的新兴用途。