Kumar Kamlesh, Kumar Prince, Deb Dipankar, Unguresan Mihaela-Ligia, Muresan Vlad
Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India.
Department of Chemistry, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Healthcare (Basel). 2023 Jan 10;11(2):207. doi: 10.3390/healthcare11020207.
People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
从事人工智能（AI）和机器学习（ML）的生命科学领域人员面临着前所未有的压力，需要更快地开发算法。揭示创新见解并加速突破的可能性在于使用多层次整合的大型数据集。然而，即便我们可获取的数据比以往任何时候都多，但仅有极少一部分数据得到过滤、解读、整合及分析。这项技术的主题是研究计算机如何从数据中学习并模仿人类思维过程。人工智能和机器学习既提高了学习能力，又提供了一个规模足以重新定义医疗保健未来的决策支持系统。本文对人工智能和机器学习在医疗行业的应用进行了综述，特别强调了临床、发展、行政和全球卫生方面的应用，以支持整个医疗基础设施，同时阐述了医疗保健各组成部分的影响和期望。此外，还讨论了这项技术在医疗基础设施中未来可能的发展趋势和应用范围。
