Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review Article Swipe
Xiting Liu
,
Wenqian Chen
,
Wenwen Du
,
Pengmei Li
,
Xiaoxing Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fdgth.2025.1583490
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fdgth.2025.1583490
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.
Related Topics
Concepts
Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.3389/fdgth.2025.1583490
- https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1583490/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 115
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410014275Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fdgth.2025.1583490Digital Object Identifier
- Title
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Application of artificial intelligence and machine learning in lung transplantation: a comprehensive reviewWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-01Full publication date if available
- Authors
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Xiting Liu, Wenqian Chen, Wenwen Du, Pengmei Li, Xiaoxing WangList of authors in order
- Landing page
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https://doi.org/10.3389/fdgth.2025.1583490Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1583490/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1583490/pdfDirect OA link when available
- Concepts
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Interpretability, Lung transplantation, Personalization, Artificial intelligence, Medicine, Quality of life (healthcare), Computer science, Transplantation, Process (computing), Medical physics, Machine learning, Surgery, Nursing, World Wide Web, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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115Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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