Koteluk Oliwia, Wartecki Adrian, Mazurek Sylwia, Kołodziejczak Iga, Mackiewicz Andrzej
Faculty of Medical Sciences, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznan, Poland.
Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznan, Poland.
J Pers Med. 2021 Jan 7;11(1):32. doi: 10.3390/jpm11010032.
With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.
随着每天产生的医学数据数量不断增加,对可靠的自动化评估工具的需求也日益强烈。带着很高的期望,机器学习有潜力彻底改变医学的许多领域,有助于做出更快、更正确的决策并提高当前的治疗标准。如今,机器能够分析、学习、交流并理解处理后的数据,且在医疗保健领域的应用越来越多。本综述解释了不同的模型以及机器学习和训练算法的一般过程。此外,它总结了机器学习在医学和医疗保健不同分支(放射学、病理学、药理学、传染病学、个性化决策制定等等)中最有用的应用和工具。该综述还探讨了将人工智能作为一种先进的自动化医学工具应用的未来前景和威胁。