Chan Jeremy Jia Hao, Leung Pak Wing, Kilgour Helena, Dervenis Panagiotis
Colchester Hospital, East Suffolk and North Essex NHS Foundation Trust, Colchester CO4 5JL, UK.
Wexham Park Hospital, Frimley Health NHS Foundation Trust, Slough, UK.
Ther Adv Ophthalmol. 2024 Dec 4;16:25158414241302871. doi: 10.1177/25158414241302871. eCollection 2024 Jan-Dec.
The integration of artificial intelligence (AI) in healthcare, particularly in the domain of facial processing tasks, has witnessed substantial growth in the 21st century. However, this requires sufficient appraisal for clinicians and researchers to adequately understand nomenclature and key concepts commonly used in this field. This article aims to elucidate the diverse applications of facial processing tasks, such as facial landmark extraction, face detection, face tracking, facial expression recognition and action unit detection, and their relevance to ophthalmology and other medical specialties. The keywords 'ophthalmology', 'facial artificial intelligence', 'facial recognition' and 'periorbital measurements' were used on PubMed and Ovid, between September 2012 and September 2022, to identify and screen for eligible articles. Studies reporting on human patients in ophthalmology, plastic, maxillofacial and cosmetic surgery with ocular lesions whose facial biometrics were processed by AI and written in the English language were included. A total of 291 and 513 articles were identified on PubMed and Ovid respectively. Twenty articles were included for analysis in this literature review after duplicates, inaccessible articles and articles without full manuscripts were excluded. Although fully automated algorithms can share the workload in healthcare systems and relieve strains on manpower, rigorous testing is crucial, followed by the challenges of convincing management bodies that it would work in reality, coupled with the costs of implementing specialised functional hardware and software. While patients have a valid concern that it would reduce physical contact with clinicians, it is important for clinicians not to replace clinical decision-making with AI alone.
21世纪以来，人工智能（AI）在医疗保健领域，尤其是面部处理任务领域的应用有了显著增长。然而，这需要临床医生和研究人员进行充分评估，以便充分理解该领域常用的术语和关键概念。本文旨在阐明面部处理任务的各种应用，如面部地标提取、面部检测、面部跟踪、面部表情识别和动作单元检测，以及它们与眼科和其他医学专业的相关性。2012年9月至2022年9月期间，在PubMed和Ovid上使用了“眼科”“面部人工智能”“面部识别”和“眶周测量”等关键词，以识别和筛选符合条件的文章。纳入了关于眼科、整形、颌面和美容外科的人类患者的研究报告，这些患者患有眼部病变，其面部生物特征由人工智能处理，且文章为英文撰写。在PubMed和Ovid上分别确定了291篇和513篇文章。在排除重复文章、无法获取的文章和没有完整手稿的文章后，本综述纳入了20篇文章进行分析。虽然全自动算法可以分担医疗系统的工作量并减轻人力负担，但严格的测试至关重要，随后还要面对说服管理机构相信其在现实中可行的挑战，以及实施专门功能硬件和软件的成本。虽然患者确实担心这会减少与临床医生的身体接触，但临床医生不能仅用人工智能取代临床决策，这一点很重要。
