制药研究中使用的人工智能和机器学习综述

Utkarsha A. Wadighare, Swati P. Deshmukh
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摘要

人工智能和系统掌握的前沿技术已经有了相当大的发展。它减少了人类的工作量,异常地推进了人类的生活。本文介绍了利用人工智能和系统学习来增强药物发现和升级,从而使其更有组织、更正确。在医学领域,图像至关重要的专业,如放射学、病理学或肿瘤学,已经抓住了这一能够完成的任务,并在研究和开发方面部署了全面的努力,以将人工智能的适应性转换到科学包中。随着人工智能越来越广泛地应用于通常的科学成像评估工作,包括预后、分割或分类,安全高效地使用医疗人工智能软件包就显得尤为重要。这些工作支持系统获取知识和合成智能在促进药物开发和研究方法方面的工作,使其更具成本效益,或完全摆脱对临床试验的需求,因为利用这些技术可以进行模拟。这样做将有助于把愿望与炒作区分开来,并在药物开发中高质量地使用人工智能/ML 方面做出明智的选择。机器学习策略可以在没有指导输入的情况下,对庞大、异构和高维的信息集合进行复杂的潜行分析,这已被证明有助于编写商业企业应用程序。将系统掌握(尤其是深度掌握)与人类技能和经验相结合,可能是协调众多重要信息库的最佳方式。人工智能创新的强大事实挖掘能力为计算机支持的医疗计划赋予了新的本质,这些计划包含多个临床问题,高于零散的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review on artificial intelligence and machine learning used in pharmaceutical research
The cutting-edge upward push of artificial intelligence and system mastering has been of considerable size. It has reduced the human workload move forward exceptional of life exceptionally. This article describes using artificial intelligence and system learning to augment drug discovery and upgrade to lead them to more well organised and correct. In medication, specialties in which images are vitally important, like radiology, pathology or oncology, have seized the able to be done and full-size efforts in studies and development were deployed to switch the adaptness of AI to scientific packages. With AI becoming a extra widespread device for usual scientific imaging evaluation duties, together with prognosis, segmentation, or classification, the important thing for a secure and efficient use of medical AI packages. This body of work supported the jobs of system gaining knowledge of and synthetic intelligence in facilitating drug expansion and finding out methods, making them greater cost-powerful or altogether casting off the want for clinical trials, as a result of the potential to conduct simulations the usage of those technologies. Doing so will assist in separating wish from hype and lead to knowledgeable choice making at the top-quality use of AI/ML in drug development. Machine studying strategies can subterfuge complicated analyzes with huge, heterogeneous, and excessive dimensional information collections without a guide enter, which has proved helpful inside the writing commercial enterprise applications. Combining system mastering, particularly deep getting to know, with human skill and revel in is probably the great manner to coordinate numerous significant facts stores. The magnificent facts-mining capacity of AI innovation has given new essentiality to computer supported medication plans that incorporate more than one clinical concerns are higher than piecemeal data.
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