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.

本质量分析了 Transformer 神经网络架构在医疗保健中的应用，以及在医学图像、电子健康记录和生理信号等多种形式的数据分析方面所发挥的积极作用，并概述了一些使用该架构的优点和局限性，包括计算成本、模型可解释性、公平性、与人类价值观的一致性、伦理和环境影响。