REVIEW article
Front. Digit. Health
Sec. Personalized Medicine
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1583490
This article is part of the Research TopicAdvancing Personalized Healthcare Through AI and Computer Audition TechnologiesView all 3 articles
Application of Artificial Intelligence and Machine Learning in Lung Transplantation: A Comprehensive Review
Provisionally accepted
- 1China-Japan Friendship Hospital, Beijing, China
- 2Peking University, Beijing, Beijing Municipality, China
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Lung transplantation (LTx) is an effective method for treating end-stage lung disease. The management of lung transplant recipients is a complex, multi-stage process that involves preoperative, intraoperative, and postoperative phases, integrating multidimensional data such as demographics, clinical data, pathology, imaging, and omics. Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. However, these technologies face numerous challenges in real-world clinical applications, such as the quality and reliability of datasets, model interpretability, physicians' trust in the technology, and legal and ethical issues. These problems require further research and resolution so that AI and ML can more effectively enhance the success rate of LTx and improve patients' quality of life.
Keywords: Lung Transplantation, artificial intelligence, machine learning, organ allocation, prognosis
Received: 26 Feb 2025; Accepted: 21 Apr 2025.
Copyright: © 2025 Liu, Chen, Du, Li and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Pengmei Li, China-Japan Friendship Hospital, Beijing, China
Xiaoxing Wang, China-Japan Friendship Hospital, Beijing, China
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