人工智能在临床试验和药物开发中的应用:挑战与潜在进步。

Divyanshi Gupta, Pranay Wal, Ankita Wal, Sribhavani K R, Mudit Kumar, Krishna Chandra Panda, Mukesh Chandra Sharma
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引用次数: 0

摘要

人工智能(AI)是各行各业发展最快的领域之一,包括工程、建筑、医学和临床研究、航空航天等。人工智能是机器学习(ML)、深度学习(DL)和人类智能(HI)的结合,它正在彻底改变药物的发现和开发,使其更具成本效益和效率。它还被用于药物化学、分子和细胞生物学、药理学、药物动力学、制剂开发和毒理学等领域。人工智能在临床测试中发挥着至关重要的作用,它可以加强患者分层、患者样本评估和试验设计,协助识别生物标记物,确定疗效标准、剂量选择、试验长度和目标患者人群选择。本研究的主要目的是强调人工智能在临床试验和药物开发中的重要性,同时探讨人工智能在医疗保健行业中的现有挑战和潜在进步。我们进行了全面的文献综述,时间跨度从 1998 年到 2023 年。我们在 Science Direct、PubMed 和 Google Scholar 数据库中搜索了相关信息。包括 Research Gate、Nature、MDPI 和 Springer Link 在内的各种出版物提供了相关数据。本研究旨在深入了解人工智能在临床研究和药物开发中的应用及其潜力和局限性。我们还讨论了传统试验和药物开发方法的优势和主要数据限制。目前正在使用人工智能方法来克服研究障碍,消除概念或方法上的局限性。在讨论了可能存在的障碍和应对机制后,我们提出了几项建议,以帮助个人了解与临床研究和药物开发相关的挑战和困难。如果人工智能要成为临床研究和药物开发的常规工具,制药公司就必须制定最前沿的人工智能战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.

Artificial intelligence (AI) is one of the fastest-growing fields in various industries, including engineering, architecture, medical and clinical research, aerospace, and others. AI, which is a combination of machine learning (ML), deep learning (DL), and human intelligence (HI), is revolutionizing drug discovery and development by making it more cost-effective and efficient. It is also being used in fields such as medicinal chemistry, molecular and cell biology, pharmacology, pharmacokinetics, formulation development, and toxicology. AI plays a crucial role in clinical testing by enhancing patient stratification, patient sample evaluation, and trial design, assisting in the identification of biomarkers, determining efficacy criteria, dose selection, trial length, and target patient population selection. The primary objective of this study is to emphasize the importance of AI in clinical trials and drug development, while also exploring the existing challenges and potential advancements in AI within the healthcare industry. A comprehensive literature review was conducted, covering the period from 1998 to 2023. The Science Direct, PubMed, and Google Scholar databases were searched for relevant information. A variety of publications, including Research Gate, Nature, MDPI, and Springer Link, provided pertinent data. This study aimed to gain a deeper understanding of the use of AI in clinical research and drug development, as well as its potential and limitations. We also discuss the benefits and main data limitations of the traditional trial and drug development approach. AI approaches are currently being used to overcome research obstacles and eliminate conceptual or methodological limitations. After discussing possible obstacles and coping mechanisms, we provide several recommendations to help individuals understand the challenges and difficulties associated with clinical research and drug development. It is essential for pharmaceutical companies to have a cutting-edge AI strategy if AI is to become a routine tool for clinical research and drug development.

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