Artificial intelligence approaches to physiological parameter analysis in the monitoring and treatment of non-communicable diseases: A review

The wide availability of electronic medical data from multiple sources, such as clinical settings and wearable devices, evidences an important opportunity for the analysis of non-communicable diseases with high mortality rates globally and costly management expenditures, like cancer, diabetes, cardiovascular or chronic respiratory diseases. Advances in artificial intelligence (AI) have a great potential to use the information obtained from the constant monitoring of physiological parameters to create models that can serve as support in the warning of possible health complications, enabling medical professionals to provide anticipated care, thus benefiting national economies through reduction of medical expenses.The present study is a systematic review of different scientific databases taking two time periods, from 1956 to 2021 and from 2011 to 2021, to show the trends in the use of artificial intelligence algorithms and the performances obtained in the monitoring and detection of non-communicable diseases using electronic data. It is found that the analysis of cancer data predominates over that of other non-communicable diseases; also, the best performance of AI techniques was not achieved by artificial neural networks despite being the most reported computing systems in the time period considered.