Ronen Lielle, Keshavjee Shaf, Sage Andrew T
Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network.
Toronto Lung Transplant Program, Ajmera Transplant Centre, University Health Network.
Curr Opin Pulm Med. 2025 Jul 1;31(4):381-386. doi: 10.1097/MCP.0000000000001168. Epub 2025 Apr 21.
To explore the current applications of artificial intelligence and machine learning in lung transplantation, including outcome prediction, drug dosing, and the potential future uses and risks as the technology continues to evolve.
While the use of artificial intelligence (AI) and machine learning (ML) in lung transplantation is relatively new, several groups have developed models to predict short-term outcomes, such as primary graft dysfunction and time-to-extubation, as well as long-term outcomes related to survival and chronic lung allograft dysfunction. Additionally, drug dosing models for Tacrolimus levels have been designed, demonstrating proof of concept for modelling treatment as a time-series problem.
The integration of ML models with clinical decision-making has shown promise in improving post-transplant survival and optimizing donor lung utilization. As technology advances, the field will continue to evolve, with enhanced datasets supporting more sophisticated ML models, particularly through real-time monitoring of biological, biochemical, and physiological data.
探讨人工智能和机器学习在肺移植中的当前应用，包括结果预测、药物剂量确定，以及随着技术不断发展其未来潜在的用途和风险。
虽然人工智能（AI）和机器学习（ML）在肺移植中的应用相对较新，但已有多个团队开发出模型来预测短期结果，如原发性移植肺功能障碍和拔管时间，以及与生存和慢性移植肺功能障碍相关的长期结果。此外，还设计了用于他克莫司血药浓度的药物剂量模型，证明了将治疗建模为时间序列问题的概念验证。
将机器学习模型与临床决策相结合已显示出在提高移植后生存率和优化供肺利用方面的前景。随着技术的进步，该领域将不断发展，更多的数据集将支持更复杂的机器学习模型，特别是通过对生物、生化和生理数据的实时监测。
