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Peer-reviewedCC BY 4.0Journal of Clinical Medicine

Artificial Intelligence in Pulmonary Hypertension: Advancements, Opportunities, and Challenges

  • Xinyi Li1,2,3,4
  • Linyue Zhang1,2,3,4
  • Li Zhang1,2,3,4
  • Yuji Xie1,2,3,4
  • Mingxing Xie1,2,3,4
  • Mengmeng Ji1,2,3,4
  • Qing He1,2,3,4
  • Zhenni Wu1,2,3,4
  • Shuxuan Qin1,2,3,4
  • Yuman Li1,2,3,4
4 affiliations
  1. 1Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan 430022, China
  2. 2Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
  3. 3Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
  4. 4Key Laboratory of Biological Targeted Therapy, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430022, China
Open at sourceDOICite

Abstract

Pulmonary hypertension (PH) is a progressive cardiopulmonary disorder associated with high morbidity and mortality. Delayed diagnosis, heterogeneous disease phenotypes, and variable treatment responses remain major clinical challenges. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a promising tool for improving PH management. AI-based models have demonstrated promising performance in screening, diagnosis, classification, risk stratification, and prognosis prediction using multimodal data such as electrocardiography, chest radiography, computed tomography, cardiac magnetic resonance imaging, echocardiography, and electronic health records. Furthermore, AI may support individualized risk assessment, treatment-response prediction, early identification of right ventricular deterioration, and longitudinal remote monitoring. Emerging technologies, including explainable AI, federated learning, large language models, and digital twins, may further advance precision medicine in PH. Despite these advances, data quality, interpretability, privacy, and clinical implementation of AI remain challenges. This review summarizes current applications, opportunities, and challenges of AI in PH and discusses future directions for AI-assisted precision healthcare.

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Cite

BibTeXRIS
BibTeX
@article{li2026artificial,
  title = {{Artificial Intelligence in Pulmonary Hypertension: Advancements, Opportunities, and Challenges}},
  author = {Xinyi Li and Linyue Zhang and Li Zhang and Yuji Xie and Mingxing Xie and Mengmeng Ji and Qing He and Zhenni Wu and Shuxuan Qin and Yuman Li},
  journal = {Journal of Clinical Medicine},
  year = {2026},
  month = nov,
  doi = {10.3390/jcm15218707},
  url = {https://www.mdpi.com/2077-0383/15/21/8707},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Artificial Intelligence in Pulmonary Hypertension: Advancements, Opportunities, and Challenges
AU  - Xinyi Li
AU  - Linyue Zhang
AU  - Li Zhang
AU  - Yuji Xie
AU  - Mingxing Xie
AU  - Mengmeng Ji
AU  - Qing He
AU  - Zhenni Wu
AU  - Shuxuan Qin
AU  - Yuman Li
PY  - 2026
DA  - 2026/11/07
JO  - Journal of Clinical Medicine
DO  - 10.3390/jcm15218707
UR  - https://www.mdpi.com/2077-0383/15/21/8707
AB  - Pulmonary hypertension (PH) is a progressive cardiopulmonary disorder associated with high morbidity and mortality. Delayed diagnosis, heterogeneous disease phenotypes, and variable treatment responses remain major clinical challenges. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a promising tool for improving PH management. AI-based models have demonstrated promising performance in screening, diagnosis, classification, risk stratification, and prognosis prediction using multimodal data such as electrocardiography, chest radiography, computed tomography, cardiac magnetic resonance imaging, echocardiography, and electronic health records. Furthermore, AI may support individualized risk assessment, treatment-response prediction, early identification of right ventricular deterioration, and longitudinal remote monitoring. Emerging technologies, including explainable AI, federated learning, large language models, and digital twins, may further advance precision medicine in PH. Despite these advances, data quality, interpretability, privacy, and clinical implementation of AI remain challenges. This review summarizes current applications, opportunities, and challenges of AI in PH and discusses future directions for AI-assisted precision healthcare.
SN  - 2077-0383
ER  -