Merchán Gómez Beatriz, Milla Collado Lucía, Rodríguez María
Gastroenterology Department, Clínica Universidad de Navarra, Madrid, Spain.
Thoracic Surgery Department, Clínica Universidad de Navarra, Madrid, Spain.
Ann Transl Med. 2023 Aug 30;11(10):353. doi: 10.21037/atm-22-3977. Epub 2023 Jun 8.
Artificial intelligence (AI) use is becoming increasingly prevalent directly or indirectly in daily clinical practice, including esophageal cancer (EC) diagnosis and treatment. Although the limits of its adoption and their clinical benefits are still unknown, any physician related to EC patients' management should be aware of the status and future perspectives of AI use in their field. The purpose of this review is to summarize the existing literature regarding the role of AI in diagnosis and treatment of EC. We have focused on the aids AI entails in the management of this pathology and we have tried to offer an updated perspective to maximize current applications and to identify potential future uses of it.
Data concerning AI applied to EC diagnosis and treatment is not limited, including direct (those specifically related to them) and indirect (those referring to other specialties as radiology or pathology), applications. However, the clinical relevance of the discussed and presented models is still unknown. We performed a research in PubMed of English and Spanish written studies from January 1970 to June 2022.
Information regarding the role of AI in EC diagnosis and treatment has increased exponentially in recent years. Several models, including different variables and features have been investigated and some of them internally and externally validated. However, the main challenge remains to apply and introduce all these data into clinical practice, and, as some of the discussed studies argue, if the models are able to enhance experienced endoscopists' judgement. Although AI use is increasing steadily in different medical specialties, the truth is, most of the time, the gap between model development and clinical implementation is not closed. Learning to understand the routinely application of AI, as well as future improvements, would lead to a broadened adoption.
Physicians should be aware of the multiple current clinical uses of AI in EC diagnosis and treatment and should take part in their clinical application and future developments to enhance patient care.
人工智能（AI）在日常临床实践中直接或间接的应用日益普遍，包括食管癌（EC）的诊断和治疗。尽管其应用的局限性及其临床益处尚不清楚，但任何参与EC患者管理的医生都应了解AI在其领域的应用现状和未来前景。本综述的目的是总结关于AI在EC诊断和治疗中作用的现有文献。我们重点关注了AI在这种疾病管理中所带来的辅助作用，并试图提供一个最新的视角，以最大化当前的应用，并确定其潜在的未来用途。
关于AI应用于EC诊断和治疗的数据并不局限，包括直接应用（那些与EC诊断和治疗直接相关的）和间接应用（那些涉及放射学或病理学等其他专业的）。然而，所讨论和展示的模型的临床相关性仍然未知。我们在PubMed上检索了1970年1月至2022年6月期间以英文和西班牙文撰写的研究。
近年来，关于AI在EC诊断和治疗中作用的信息呈指数级增长。已经研究了几种模型，包括不同的变量和特征，其中一些模型进行了内部和外部验证。然而，主要挑战仍然是将所有这些数据应用并引入临床实践，而且，正如一些所讨论的研究所指出的，这些模型是否能够增强经验丰富的内镜医师的判断。尽管AI在不同医学专业中的应用正在稳步增加，但事实是，大多数时候，模型开发与临床应用之间的差距并未弥合。学会理解AI的常规应用以及未来的改进，将有助于更广泛地采用AI。
医生应了解AI目前在EC诊断和治疗中的多种临床应用，并应参与其临床应用和未来发展，以提高患者护理水平。
