With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of data, including medical imaging, structured and unstructured Electronic Health Records (EHR), social media, physiological signals, and biomolecular sequences. Those models could help in clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. We identified relevant studies using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.

翻译：随着人工智能（AI）日益渗透到社会各领域，包括医疗卫生，Transformer神经网络架构的采用正在迅速改变众多应用场景。Transformer最初是为解决通用自然语言处理（NLP）任务而开发的深度学习架构，随后被广泛应用于包括医疗在内的多个领域。本综述论文概述了该架构如何被用于分析多种形式的数据，包括医学影像、结构化和非结构化电子健康记录（EHR）、社交媒体数据、生理信号以及生物分子序列。这些模型可辅助临床诊断、报告生成、数据重建以及药物/蛋白质合成。我们依据系统综述和荟萃分析优先报告条目（PRISMA）指南进行了相关研究筛选。此外，我们讨论了在医疗领域使用Transformer的优势与局限性，并审视了计算成本、模型可解释性、公平性、与人类价值观的一致性、伦理影响及环境影响等问题。