Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions

Abstract

This study attempts to categorise research conducted in the area of: use of machine learning in healthcare, using a systematic mapping study methodology. In our attempt, we reviewed literature from top journals, articles, and conference papers by using the keywords use of machine learning in healthcare. We queried Google Scholar, resulted in 1400 papers, and then categorised the results on the basis of the objective of the study, the methodology adopted, type of problem attempted and disease studied. As a result we were able to categorize study in five different categories namely, interpretable ML, evaluation of medical images, processing of EHR, security/privacy framework, and transfer learning. In the study we also found that most of the authors have studied cancer, and one of the least studied disease was epilepsy, evaluation of medical images is the most researched and a new field of research, Interpretable ML/Explainable AI, is gaining momentum. Our basic intent is to provide a fair idea to future researchers about the field and future directions.

Information Theoretic Measures Applied to Deep Learning Models

“…The figure below shows the architecture of a vanilla neural network. Deep learning has seen huge success in computer vision [5,6,7], NLP [8,9,10,11] and in the health care industry [12,13,14,15].…”

BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management

“…In the second stage, the model's performance was assessed for all permutations of each feature type. Angle features presented 512 permutations [29], while PROM features featured 128 permutations [27]. The final stage encompassed constructing an optimal feature set, in the sense of producing the highest F1-measure among all possible permutations of features in the corresponding model assessment, finalising model training, and executing an ablation experiment to analyse each feature's impact on the final model's accuracy.…”

“…Machine learning (ML), a subset of artificial intelligence (AI), employs data and algorithms to discern patterns akin to human intelligence. In the medical sphere, ML is increasingly applied for prognosis, diagnosis, and personalised treatments [29]. It has also found utility in diagnosing and prognosticating LBP, though its precision lags behind clinical classification [30].…”

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

“…The significance of data normalization in developing precise predictive models has been investigated across multiple machine learning algorithms [41], including Nearest Neighbors (NN) [42], Artificial Neural Networks (ANN) [43] and Support Vector Machines (SVM) [44]. Several researchers have confirmed the positive impact of data normalization on enhancing classification performance in various domains [45]. Examples include medical data classification [46,47], multimodal biometrics systems [48], vehicle classification [49], faulty motor detection [50], stock market prediction [51], leaf classification [52], credit approval data classification [53], genomics [54], and other application areas [55,56].…”