Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350.
Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350; Department of Surgery, Division of Vascular Surgery, VA Loma Linda Healthcare System, 11201 Benton Street, Loma Linda, CA 92357.
Semin Vasc Surg. 2023 Sep;36(3):430-434. doi: 10.1053/j.semvascsurg.2023.07.003. Epub 2023 Aug 1.
Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning models to learn without a priori assumption, or without prior learning, to connect seemingly unrelated information mixes excitement alongside hesitation to fully understand AI's limitations. Bias, ranging from data collection and input to algorithm development to finally human review of algorithm output affects AI's application to clinical patient presents unique challenges that differ significantly from biases in traditional analyses. Algorithm fairness, a new field of research within AI, aims to mitigate bias by evaluating the data at the preprocessing stage, optimizing during algorithm development, and evaluating algorithm output at the postprocessing stage. As the field continues to develop, being cognizant of the inherent biases and limitations related to black box decision making, biased data sets agnostic to patient-level disparities, wide variation of present methodologies, and lack of common reporting standards will require ongoing research to provide transparency to AI and its applications.
人工智能 (AI) 的应用彻底改变了大数据的利用方式，尤其是在患者护理方面。深度学习模型无需先验假设或预先学习即可连接看似不相关的信息，这一潜力令人兴奋，但同时也让人犹豫不决，难以完全理解 AI 的局限性。从数据收集和输入到算法开发，再到最终对算法输出进行人工审查，偏差会影响 AI 在临床患者中的应用，这带来了独特的挑战，与传统分析中的偏差有很大的不同。算法公平性是 AI 领域内的一个新研究领域，旨在通过在预处理阶段评估数据、在算法开发过程中进行优化以及在后期处理阶段评估算法输出来减轻偏差。随着该领域的不断发展，人们需要意识到与黑盒决策相关的固有偏差和局限性、对患者层面差异不可知的有偏差数据集、目前方法的广泛差异以及缺乏通用报告标准，这将需要持续研究，以提高 AI 及其应用的透明度。
