Proceedings of the 2024 Transplant AI Symposium.

Frontiers in transplantation Pub Date : 2024-08-29 eCollection Date: 2024-01-01 DOI:10.3389/frtra.2024.1399324
Sara Naimimohasses, Shaf Keshavjee, Bo Wang, Mike Brudno, Aman Sidhu, Mamatha Bhat
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Abstract

With recent advancements in deep learning (DL) techniques, the use of artificial intelligence (AI) has become increasingly prevalent in all fields. Currently valued at 9.01 billion USD, it is a rapidly growing market, projected to increase by 40% per annum. There has been great interest in how AI could transform the practice of medicine, with the potential to improve all healthcare spheres from workflow management, accessibility, and cost efficiency to enhanced diagnostics with improved prognostic accuracy, allowing the practice of precision medicine. The applicability of AI is particularly promising for transplant medicine, in which it can help navigate the complex interplay of a myriad of variables and improve patient care. However, caution must be exercised when developing DL models, ensuring they are trained with large, reliable, and diverse datasets to minimize bias and increase generalizability. There must be transparency in the methodology and extensive validation of the model, including randomized controlled trials to demonstrate performance and cultivate trust among physicians and patients. Furthermore, there is a need to regulate this rapidly evolving field, with updated policies for the governance of AI-based technologies. Taking this in consideration, we summarize the latest transplant AI developments from the Ajmera Transplant Center's inaugural symposium.

2024 年移植人工智能研讨会论文集。
随着近年来深度学习(DL)技术的发展,人工智能(AI)在各个领域的应用日益普及。目前,人工智能市场价值达 90.1 亿美元,是一个快速增长的市场,预计每年将增长 40%。人们对人工智能如何改变医疗实践产生了浓厚的兴趣,因为人工智能有可能改善所有医疗领域,从工作流程管理、可及性和成本效率,到提高预后准确性的强化诊断,从而实现精准医疗。人工智能在移植医学领域的应用前景尤其广阔,它可以帮助驾驭无数变量的复杂相互作用,改善患者护理。不过,在开发 DL 模型时必须小心谨慎,确保使用大型、可靠和多样化的数据集对其进行训练,以尽量减少偏差并提高可推广性。方法必须透明,并对模型进行广泛验证,包括随机对照试验,以证明其性能并培养医生和患者之间的信任。此外,有必要对这一快速发展的领域进行监管,更新人工智能技术的管理政策。考虑到这一点,我们总结了阿杰梅拉移植中心首届研讨会上最新的移植人工智能发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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