Potential Value and Impact of Machine Learning in Clinical Classification and Prediction

Dear Colleagues, 

Recently, artificial intelligence (AI) has been widely used in medicine and healthcare. In machine learning, classification/prediction is one of the main areas of AI. Currently, there is active research on existing prediction models based on machine-learning methods. Machine-learning algorithms have improved the ability to predict the risk of complex diseases. This improved predictive capability stems from the ability of machine-learning algorithms to process multidimensional data. A great deal of research has been conducted on predicting the future state of patients. Future applications of machine learning predictive models may help manage complex diseases by providing tissue-specific targets for customized, preventive interventions.

Machine learning can also be used to aid in diagnosis and prognosis. Recent efforts to use machine learning for diagnosis have shifted from classification of a given disease to differential diagnosis. In particular, data mining techniques are now increasingly used in clinical diagnosis, and they have the potential to support this area.

We are pleased to invite you to contribute to this Special Issue on the potential value and impact of machine learning in clinical classification and prediction with original contributions and review articles focusing on recent and related advances in the field, including the latest trends and future developments.

Dr. Chunhung Richard Lin
Guest Editor

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