超越人类的极限:利用人工智能优化肾移植中的免疫抑制。

IF 1.6 Q2 MEDICINE, GENERAL & INTERNAL
Journal of clinical medicine research Pub Date : 2023-09-01 Epub Date: 2023-09-30 DOI:10.14740/jocmr5012
Debargha Basuli, Sasmit Roy
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)技术的结合正在彻底改变肾移植领域。人工智能使机器具备类似人类的认知能力,而ML使计算机能够从数据中学习。移植中的挑战,如器官分配和同种异体移植功能或排斥反应的预测,可以通过人工智能算法来解决。这些算法可以优化免疫抑制方案并改善患者护理。这篇全面的文献综述概述了最近关于利用AI和ML技术优化肾移植免疫抑制的所有研究。通过开发个性化和数据驱动的免疫抑制方案,临床医生可以做出明智的决定并加强患者护理。然而,ML模型也存在局限性,如数据质量、小样本量、验证、计算复杂性和可解释性。未来的研究应该验证和完善不同人群和治疗持续时间的人工智能模型。AI和ML有可能通过优化免疫抑制和改善结果来彻底改变肾移植。人工智能驱动的算法实现了个性化和数据驱动的免疫抑制协议,增强了患者护理和决策能力。限制包括数据质量、小样本量、验证、计算复杂性和ML模型的可解释性。需要进一步的研究来验证和增强不同人群的人工智能模型和长期给药决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation.

The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.

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