From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Abstract

The medicine and healthcare sector has been evolving and advancing very fast. The advancement has been initiated and shaped by the applications of data-driven, robust, and efficient machine learning (ML) to deep learning (DL) technologies. ML in the medical sector is developing quickly, causing rapid progress, reshaping medicine, and improving clinician and patient experiences. ML technologies evolved into data-hungry DL approaches, which are more robust and efficient in dealing with medical data. This article reviews some critical data-driven aspects of machine intelligence in the medical field. In this direction, the article illustrated the recent progress of data-driven medical science using ML to DL in two categories: firstly, the recent development of data science in medicine with the use of ML to DL and, secondly, the chabot technologies in healthcare and medicine, particularly on ChatGPT. Here, we discuss the progress of ML, DL, and the transition requirements from ML to DL. To discuss the advancement in data science, we illustrate prospective studies of medical image data, newly evolved DL interpretation data from EMR or EHR, big data in personalized medicine, and dataset shifts in artificial intelligence (AI). Simultaneously, the article illustrated recently developed DL-enabled ChatGPT technology. Finally, we summarize the broad role of ML and DL in medicine and the significant challenges for implementing recent ML to DL technologies in healthcare. The overview of the data-driven paradigm shift in medicine using ML to DL technologies in the article will benefit researchers immensely.