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Peer-reviewedCC BYFrontiers in artificial intelligence

Transformer models in cardiovascular medicine: current applications, translational challenges, and future directions.

  • Yang Yan1
  • Gaoxiang Li1
  • Taiwei Chen1
  • Jinchuan Yan2
  • Chen Wang3
  • Ancai Yuan1
  • Jiawen Zhang3
  • Jun Pu1
  1. 1Department of Cardiology, State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  2. 2Department of Cardiology, Zhenjiang Ruikang Hospital, Zhenjiang, Jiangsu, China.
  3. 3Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China.
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Abstract

Cardiovascular diseases remain a leading cause of mortality and morbidity worldwide, highlighting the need for improved early detection, accurate diagnosis, and personalized therapeutic strategies. With advances in artificial intelligence (AI), Transformer-based models have attracted increasing interest because they can model long-range dependencies, support large-scale pre-training, and integrate heterogeneous data. Originally developed for natural language processing, Transformers have been extended to medical image analysis, physiological signal interpretation, and clinical text mining. In cardiovascular medicine, these models have been applied to electrocardiogram interpretation, cardiac imaging, electronic health record analysis, multimodal data integration, digital-twin development, and large language model-enabled workflows. Although many studies report promising performance, clinical value remains contingent on data quality, external validation, calibration, workflow integration, and trustworthy governance. This narrative review provides a focused overview of Transformer-based models in cardiovascular medicine, critically evaluates their translational limitations, and outlines priorities for clinically useful and responsible cardiovascular AI.

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Cite

BibTeXRIS
BibTeX
@article{yan2026transformer,
  title = {{Transformer models in cardiovascular medicine: current applications, translational challenges, and future directions.}},
  author = {Yang Yan and Gaoxiang Li and Taiwei Chen and Jinchuan Yan and Chen Wang and Ancai Yuan and Jiawen Zhang and Jun Pu},
  journal = {Frontiers in artificial intelligence},
  year = {2026},
  month = oct,
  doi = {10.3389/frai.2026.1962796},
  url = {https://europepmc.org/article/MED/42923828},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Transformer models in cardiovascular medicine: current applications, translational challenges, and future directions.
AU  - Yang Yan
AU  - Gaoxiang Li
AU  - Taiwei Chen
AU  - Jinchuan Yan
AU  - Chen Wang
AU  - Ancai Yuan
AU  - Jiawen Zhang
AU  - Jun Pu
PY  - 2026
DA  - 2026/10/08
JO  - Frontiers in artificial intelligence
DO  - 10.3389/frai.2026.1962796
UR  - https://europepmc.org/article/MED/42923828
AB  - Cardiovascular diseases remain a leading cause of mortality and morbidity worldwide, highlighting the need for improved early detection, accurate diagnosis, and personalized therapeutic strategies. With advances in artificial intelligence (AI), Transformer-based models have attracted increasing interest because they can model long-range dependencies, support large-scale pre-training, and integrate heterogeneous data. Originally developed for natural language processing, Transformers have been extended to medical image analysis, physiological signal interpretation, and clinical text mining. In cardiovascular medicine, these models have been applied to electrocardiogram interpretation, cardiac imaging, electronic health record analysis, multimodal data integration, digital-twin development, and large language model-enabled workflows. Although many studies report promising performance, clinical value remains contingent on data quality, external validation, calibration, workflow integration, and trustworthy governance. This narrative review provides a focused overview of Transformer-based models in cardiovascular medicine, critically evaluates their translational limitations, and outlines priorities for clinically useful and responsible cardiovascular AI.
SN  - 2624-8212
ER  -