Transformers in Healthcare: A Survey
Abstract
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.
Cite
@article{arxiv.2307.00067,
title = {Transformers in Healthcare: A Survey},
author = {Subhash Nerella and Sabyasachi Bandyopadhyay and Jiaqing Zhang and Miguel Contreras and Scott Siegel and Aysegul Bumin and Brandon Silva and Jessica Sena and Benjamin Shickel and Azra Bihorac and Kia Khezeli and Parisa Rashidi},
journal= {arXiv preprint arXiv:2307.00067},
year = {2024}
}