TIGP (BIO)—Advancing Clinical Decision Support through Explainable AI and Machine Learning

Abstract

The innovative applications of artificial intelligence (AI) and machine learning (ML) in medical informatics are facilitating the development of personalized medicine and healthcare. Machine learning algorithms have found extensive utility in clinical decision support and healthcare research. Consequently, this talk will introduce several healthcare research topics that leverage explainable artificial intelligence. First and foremost, the applications of machine learning algorithms play a crucial role in advancing clinical decision support systems for various diseases. These include the prediction of conditions such as preeclampsia, in vitro fertilization success in pregnancies and diabetic retinopathy. Moreover, machine learning algorithms can be instrumental in epidemiology analyses with public health contexts, aiding in the prediction of dengue fever outbreaks and the management of risks associated with the COVID-19 virus. In summary, applications of artificial intelligence, particularly explainable machine learning algorithms, have proven highly effective in the development of clinical decision support systems. This field holds great promise for further discoveries, interventions and future directions that can be anticipated in the near future.

Keywords: clinical decision support, medical informatics, machine learning, explainable AI

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