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Artificial intelligence (AI) systems for interpreting complex medical datasets
Author(s) -
Altman RB
Publication year - 2017
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.650
Subject(s) - computer science , artificial intelligence , data science , intellectual property , liability , machine learning , property (philosophy) , noisy data , data mining , philosophy , finance , epistemology , economics , operating system
Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability.