Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Abstract

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

Keywords: XAI; artificial intelligence; computer vision; deep learning; healthcare; medical imaging; precision medicine; supervised learning.

Figure 1

Working flow of SML process in healthcare.

Figure 2

The probable trend of revenue generation trend in the health and biomedical sectors.

Figure 3

Major data types for supervised machine learning in healthcare research.

Figure 4

SML-empowered clinical trial procedure. AI = Artificial Intelligence.

Figure 5

The imaging modality and body parts for ML and CV for clinical studies. (A) Different imaging modalities can be used for CV and image processing. (B) Imaging from different body parts of humans.

Figure 6

The deep learning application in a medical imaging application.

Figure 7

Potential clinical uses of Computer Vision in medical imaging techniques and the corresponding possible tasks.

Figure 8

Data preparation pipeline for Supervised Machine Learning solution.

Figure 9

Major AI method used in medical image analysis. (A) Method used in image analysis and quantitative medical image data analysis, and (B) percentage of each method currently used in medical imaging analysis. Some statistics were also provided by Kumar et al. [1]. The abbreviations used in the figure are as follows: Naive Bayes (NB), support vector machines (SVM), regression tree (RT), random forest (RF), classification tree (CT), classification and regression tree (CART), K-nearest neighbor (KNN), principal component analysis (PCA), hidden Markov model (HMM), Gaussian mixture (GM or GM model), fuzzy logic (FL), regions with CNN (R-CNN), deep learning (DL), self-organized machines (SOM), and artificial neural network (ANN).

Figure 10

The schematic diagram of XAI for a machine learning-based model to achieve a transparent and trustworthy model.