Emerging point of care devices and artificial intelligence: Prospects and challenges for public health

Stranieri, Andrew; Venkatraman, Sitalakshmi; Minicz, John; Zarnegar, Armita; Firmin, Sally; Balasubramanian, Venki; Jelinek, Herbert F.

doi:10.1016/j.smhl.2022.100279

Cited by 12 publications

(4 citation statements)

References 62 publications

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“…Consequently, specific battery-operated devices can operate for ten years. The technology group known as LPWAN is now attracting significant interest in Health Care Systems (Stranieri et al, 2022).…”

Section: Lora Communication Networkmentioning

confidence: 99%

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

Lavanya,

Subba Reddy,

Selvakumar

et al. 2024

JISIS

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In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

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Section: Lora Communication Networkmentioning

confidence: 99%

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

Lavanya,

Subba Reddy,

Selvakumar

et al. 2024

JISIS

View full text Add to dashboard Cite

show abstract

“…Based on the systematic literature review (SLR), a variety of challenges were discussed in the past research including ethical issues [91], the adoption challenges of mobile health wearable devices [92], the scaling of the existing disease surveillance system for public access [93], expandability and public data privacy concerns [94], and the lack of health policies and intelligent systems [95,96]. The healthcare industry is growing each day; thus, the existing players need to adopt new business models [94].…”

Section: Artificial Intelligence and Public Healthcare Systems In Dev...mentioning

confidence: 99%

“…Around sixty percent of the healthcare industry is not prepared to adapt to the integrated and collaborated environment and is therefore unable to adopt and implement the analytics. This sector does not have advance technology and lacks in the adoption of analytical tools/methods as compared to different sectors/fields, such as the retail and banking field [92]. The public health ecosystem is slowly transforming, where the major focus is on the improvisation of the value chains through the usage of digital technology [93].…”

Section: Artificial Intelligence and Public Healthcare Systems In Dev...mentioning

confidence: 99%

Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries

Joshi

Sharma

Das³

et al. 2022

Sustainability

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This study work is among the few attempts to understand the significance of AI and its implementation barriers in the healthcare systems in developing countries. Moreover, it examines the breadth of applications of AI in healthcare and medicine. AI is a promising solution for the healthcare industry, but due to a lack of research, the understanding and potential of this technology is unexplored. This study aims to determine the crucial AI implementation barriers in public healthcare from the viewpoint of the society, the economy, and the infrastructure. The study used MCDM techniques to structure the multiple-level analysis of the AI implementation. The research outcomes contribute to the understanding of the various implementation barriers and provide insights for the decision makers for their future actions. The results show that there are a few critical implementation barriers at the tactical, operational, and strategic levels. The findings contribute to the understanding of the various implementation issues related to the governance, scalability, and privacy of AI and provide insights for decision makers for their future actions. These AI implementation barriers are encountered due to the wider range of system-oriented, legal, technical, and operational implementations and the scale of the usage of AI for public healthcare.

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“…Point-of-care applications are moving toward more decentralized clinical diagnostic methods and approaches that are portable, selective, sensitive, highly effective, and produce accurate analytical results in a timely manner when compared to traditional laboratory testing; hence, they are attracting much attention in the biomedical field [ 1 , 2 , 3 ]. Over the past several years, the number of point-of-care testing platforms has steadily increased, aiding in their application in emergency and accident cases.…”

Section: Introductionmentioning

confidence: 99%

Label-Free DNA Biosensor Based on Reduced Graphene Oxide and Gold Nanoparticles

et al. 2023

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Currently available DNA detection techniques frequently require compromises between simplicity, speed, accuracy, and cost. Here, we propose a simple, label-free, and cost-effective DNA detection platform developed at screen-printed carbon electrodes (SPCEs) modified with reduced graphene oxide (RGO) and gold nanoparticles (AuNPs). The preparation of the detection platform involved a two-step electrochemical procedure based on GO reduction onto SPCEs followed by the electrochemical reduction of HAuCl4 to facilitate the post-grafting reaction with AuNPs. The final sensor was fabricated by the simple physical adsorption of a single-stranded DNA (ssDNA) probe onto a AuNPs–RGO/SPCE electrode. Each preparation step was confirmed by morphological and structural characterization using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy, respectively. Furthermore, the electrochemical properties of the modified electrodes have been investigated by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results demonstrated that the introduction of AuNPs onto RGO/SPCEs led to an enhancement in surface conductivity, a characteristic that favored an increased sensitivity in detection. The detection process relied on the change in the electrochemical signal induced by the binding of target DNA to the bioreceptor and was particularly monitored by the change in the charge transfer resistance of a [Fe(CN)6]4–/3– redox couple added in the test solution.

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Emerging point of care devices and artificial intelligence: Prospects and challenges for public health

Cited by 12 publications

References 62 publications

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries

Label-Free DNA Biosensor Based on Reduced Graphene Oxide and Gold Nanoparticles

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