Liu T Y Alvin, Wu Jo-Hsuan
Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, United States.
Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA, United States.
Front Med (Lausanne). 2022 Jun 28;9:845522. doi: 10.3389/fmed.2022.845522. eCollection 2022.
Medical specialties with access to a large amount of imaging data, such as ophthalmology, have been at the forefront of the artificial intelligence (AI) revolution in medicine, driven by deep learning (DL) and big data. With the rise of AI and big data, there has also been increasing concern on the issues of bias and privacy, which can be partially addressed by low-shot learning, generative DL, federated learning and a "model-to-data" approach, as demonstrated by various groups of investigators in ophthalmology. However, to adequately tackle the ethical and societal challenges associated with the rise of AI in ophthalmology, a more comprehensive approach is preferable. Specifically, AI should be viewed as sociotechnical, meaning this technology shapes, and is shaped by social phenomena.
像眼科这样能够获取大量影像数据的医学专科,在深度学习(DL)和大数据的推动下,一直处于医学人工智能(AI)革命的前沿。随着AI和大数据的兴起,人们也越来越关注偏差和隐私问题,眼科领域的不同研究团队已证明,少样本学习、生成式DL、联邦学习和“模型到数据”方法可以部分解决这些问题。然而,要充分应对眼科领域AI兴起所带来的伦理和社会挑战,更全面的方法更为可取。具体而言,应将AI视为社会技术,即这项技术塑造社会现象,同时也受社会现象影响。