How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

被引:68
作者
Koteluk, Oliwia [1 ]
Wartecki, Adrian [1 ]
Mazurek, Sylwia [2 ,3 ]
Kolodziejczak, Iga [4 ]
Mackiewicz, Andrzej [2 ,3 ]
机构
[1] Poznan Univ Med Sci, Chair Med Biotechnol, Fac Med Sci, PL-61701 Poznan, Poland
[2] Poznan Univ Med Sci, Chair Med Biotechnol, Dept Canc Immunol, PL-61701 Poznan, Poland
[3] Greater Poland Canc Ctr, Dept Canc Diagnost & Immunol, PL-61866 Poznan, Poland
[4] Med Univ Warsaw, Postgrad Sch Mol Med, PL-02091 Warsaw, Poland
关键词
machine learning; artificial intelligence; bioinformatics; medicine; algorithm; decision making; personalized medicine; data processing; data mining; personalized treatment; DIMENSIONALITY REDUCTION; HEALTH-CARE; CLASSIFICATION; PREDICTION; INTEGRATION; ALGORITHMS; SYSTEM;
D O I
10.3390/jpm11010032
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:1 / 22
页数:22
相关论文
共 119 条
[1]   State-of-the-art in artificial neural network applications: A survey [J].
Abiodun, Oludare Isaac ;
Jantan, Aman ;
Omolara, Abiodun Esther ;
Dada, Kemi Victoria ;
Mohamed, Nachaat AbdElatif ;
Arshad, Humaira .
HELIYON, 2018, 4 (11)
[2]   Improving risk prediction in heart failure using machine learning [J].
Adler, Eric D. ;
Voors, Adriaan A. ;
Klein, Liviu ;
Macheret, Fima ;
Braun, Oscar O. ;
Urey, Marcus A. ;
Zhu, Wenhong ;
Sama, Iziah ;
Tadel, Matevz ;
Campagnari, Claudio ;
Greenberg, Barry ;
Yagil, Avi .
EUROPEAN JOURNAL OF HEART FAILURE, 2020, 22 (01) :139-147
[3]   A State-of-the-Art Survey on Deep Learning Theory and Architectures [J].
Alom, Md Zahangir ;
Taha, Tarek M. ;
Yakopcic, Chris ;
Westberg, Stefan ;
Sidike, Paheding ;
Nasrin, Mst Shamima ;
Hasan, Mahmudul ;
Van Essen, Brian C. ;
Awwal, Abdul A. S. ;
Asari, Vijayan K. .
ELECTRONICS, 2019, 8 (03)
[4]   Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections [J].
Altini, Nicola ;
Cascarano, Giacomo Donato ;
Brunetti, Antonio ;
Marino, Francescomaria ;
Rocchetti, Maria Teresa ;
Matino, Silvia ;
Venere, Umberto ;
Rossini, Michele ;
Pesce, Francesco ;
Gesualdo, Loreto ;
Bevilacqua, Vitoantonio .
ELECTRONICS, 2020, 9 (03)
[5]  
[Anonymous], FDAS COMPR EFF ADV N
[6]  
[Anonymous], 2017, MODELS MACHINE LEARN
[7]  
[Anonymous], 2008, CONCISE ENCY STAT, P364
[8]  
[Anonymous], 2006, APPL REGRESSION ANAL, P235
[9]  
Aron J., 2011, New Sci, V212, P24, DOI [10.1016/S0262-4079(11)62647-X, DOI 10.1016/S0262-4079(11)62647-X]
[10]   Overview and comparative study of dimensionality reduction techniques for high dimensional data [J].
Ayesha, Shaeela ;
Hanif, Muhammad Kashif ;
Talib, Ramzan .
INFORMATION FUSION, 2020, 59 :44-58