Clinical Applications of Artificial Intelligence—An Updated Overview

Abstract

Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

Neurotransmitters—Key Factors in Neurological and Neurodegenerative Disorders of the Central Nervous System

“…Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common type of dementia, closely related to genetic factors and age. It predominantly affects neocortical regions and is characterized by progressively episodic memory loss, significant behavioral changes, and a very high mortality rate [ 90 , 104 , 105 , 106 , 107 , 108 , 109 , 110 ]. Nevertheless, the pathogenesis of AD remains elusive and mainly rests on the observed accumulations of amyloid beta into plaques and hyperphosphorylated tau protein aggregates.…”

Recent Advances in Managing Spinal Intervertebral Discs Degeneration

“…Artificial Intelligence (AI) has increasingly become popular in the technological world, bringing promising progress to the medical system. AI has the potential to improve the prevention, screening, and treatment of diseases and the prediction of their prognosis, assisting healthcare professionals in making personalized decisions for each patient [ 123 , 124 , 125 , 126 , 127 , 128 ]. Even though the use of AI and machine learning (ML) techniques has not been widely investigated or adopted to diagnose spinal pain [ 129 ], a couple of studies have been recently published in the field.…”

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

“…On the other hand, ML represents a subset of AI that uses statistical techniques that enable computers to improve their predictions and performance concerning a specific task. Moving further, deep learning (DL) is a subdiscipline of ML employing algorithms that can train themselves through a sequential chain of pivotal features from input data [ 31 , 32 , 33 ]. The relationship between AI, ML, and DL has been schematically represented in Figure 1 .…”