A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap, Iuliu Alexandru; Oniga, Stefan

doi:10.3390/ijerph191811413

Cited by 16 publications

(7 citation statements)

References 78 publications

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“…Technology advances exponentially, but humans are linear, making it difficult to adapt to new systems. The narrative must shift from e-health/telemedicine to fitness devices, machine learning, artificial intelligence, blockchain, and automation [ 62 , 116 , 117 ]. Most healthcare providers fail to provide whole-person care.…”

Section: Challenges In Healthcare Systems That Need To Be Accounted F...mentioning

confidence: 99%

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems

Hurvitz,

Ilan

2023

Clinics and Practice

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The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a “nice to have” tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make “nice to have,” “assists,” and “ease processes” insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems’ malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system’s costs.

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Section: Challenges In Healthcare Systems That Need To Be Accounted F...mentioning

confidence: 99%

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems

Hurvitz,

Ilan

2023

Clinics and Practice

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“…6 By examining enormous datasets, DL models can identify patterns and offer valuable insights that can aid physicians in making informed decisions. As telemedicine and remote patient monitoring become increasingly prevalent, [7][8][9] personalized health monitoring and prediction using DL can offer a timely, precise, and uninterrupted observation of a patient's health status, allowing for the early identification of possible health problems and averting unfavorable events. 10 In recent years, significant progress has been made in the field of DL, 4,5 resulting in enhanced precision and rapidity in healthcare applications.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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“…Ecological momentary assessment (EMA), a research methodology that involves observing subjects in their natural environment [8], addresses the limitations of traditional neuroimaging techniques, which often lack real-world applicability [9]. Traditional approaches often involve simplified, decontextualized tasks that fail to capture the complexities of natural human behaviors and environments [10].…”

Section: Introductionmentioning

confidence: 99%

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

Emish,

Young

2024

Biomimetics

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Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.

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A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Cited by 16 publications

References 78 publications

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems

Deep learning for personalized health monitoring and prediction: A review

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

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