Artificial intelligence (AI) in point-of-care testing DOI Creative Commons
Tahir S. Pillay, Adil I. Khan, Sedef Yenice

и другие.

Clinica Chimica Acta, Год журнала: 2025, Номер unknown, С. 120341 - 120341

Опубликована: Май 1, 2025

The integration of artificial intelligence (AI) into point-of-care testing (POCT) represents a transformative leap in modern healthcare, addressing critical challenges diagnostic accuracy, workflow efficiency, and equitable access. While POCT has revolutionized decentralized care through rapid results, its potential is hindered by variability hurdles, resource constraints. AI technologies-encompassing machine learning, deep natural language processing-offer robust solutions: convolutional neural networks improve malaria detection sub-Saharan Africa to 95 % sensitivity, while predictive analytics reduce device downtime 20 resource-limited settings. AI-driven decision support systems curtail antibiotic misuse 40 real-time data synthesis, portable devices enable anaemia screening rural India with 94 slashing delays from weeks hours. Despite these advancements, persist, including privacy risks, algorithmic opacity, infrastructural gaps low- middle-income countries. Explainable frameworks blockchain encryption are building clinician trust ensuring regulatory compliance. Future directions emphasize the convergence Internet Things (IoT) for diagnostics, as demonstrated AI-IoT forecasting dengue outbreaks 14 days advance. Personalized medicine, powered genomic wearable integration, further underscores tailor therapies, reducing cardiovascular events 25 %. Realizing this vision demands interdisciplinary collaboration, ethical governance, implementation bridge global health disparities. By harmonizing innovation accessibility, AI-enhanced emerges cornerstone proactive, patient-centered poised democratize diagnostics drive sustainable equity worldwide.

Язык: Английский

Microbiome Dynamics: A Paradigm Shift in Combatting Infectious Diseases DOI Open Access
Mohamed Kamel,

Sami Aleya,

Majed Alsubih

и другие.

Journal of Personalized Medicine, Год журнала: 2024, Номер 14(2), С. 217 - 217

Опубликована: Фев. 18, 2024

Infectious diseases have long posed a significant threat to global health and require constant innovation in treatment approaches. However, recent groundbreaking research has shed light on previously overlooked player the pathogenesis of disease-the human microbiome. This review article addresses intricate relationship between microbiome infectious unravels its role as crucial mediator host-pathogen interactions. We explore remarkable potential harnessing this dynamic ecosystem develop innovative strategies that could revolutionize management diseases. By exploring latest advances emerging trends, aims provide new perspective combating by targeting

Язык: Английский

Процитировано

28

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction DOI Creative Commons
Ashima Kukkar, Yugal Kumar, Jasminder Kaur Sandhu

и другие.

Diagnostics, Год журнала: 2024, Номер 14(6), С. 624 - 624

Опубликована: Март 15, 2024

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It notable concern for developing countries due to its low diagnosis rate. has the most astounding mortality level as compared other diseases tremendous platelet depletion. Hence, it can be categorized life-threatening fever same class of fevers. Additionally, been shown dengue shares many symptoms flu-based On hand, research community closely monitoring popular fields related IoT, fog, cloud computing prediction diseases. cloud-based technologies are used constructing number health care systems. Accordingly, in this study, DengueFog system was created based on fog detection sickness. proposed includes weighted random forest (WRF) classifier monitor predict infection. The system’s efficacy evaluated using data This dataset gathered between 2016 2018 from several hospitals Delhi-NCR region. accuracy, F-value, recall, precision, error rate, specificity metrics were assess simulation results suggested system. demonstrated with WRF outperforms traditional classifiers.

Язык: Английский

Процитировано

5

Biosensors and Wearable Technologies for Early Detection and Monitoring of Tropical Diseases DOI
Matthew Chidozie Ogwu, Sylvester Chibueze Izah

Health information science, Год журнала: 2025, Номер unknown, С. 57 - 81

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Artificial intelligence (AI) in point-of-care testing DOI Creative Commons
Tahir S. Pillay, Adil I. Khan, Sedef Yenice

и другие.

Clinica Chimica Acta, Год журнала: 2025, Номер unknown, С. 120341 - 120341

Опубликована: Май 1, 2025

The integration of artificial intelligence (AI) into point-of-care testing (POCT) represents a transformative leap in modern healthcare, addressing critical challenges diagnostic accuracy, workflow efficiency, and equitable access. While POCT has revolutionized decentralized care through rapid results, its potential is hindered by variability hurdles, resource constraints. AI technologies-encompassing machine learning, deep natural language processing-offer robust solutions: convolutional neural networks improve malaria detection sub-Saharan Africa to 95 % sensitivity, while predictive analytics reduce device downtime 20 resource-limited settings. AI-driven decision support systems curtail antibiotic misuse 40 real-time data synthesis, portable devices enable anaemia screening rural India with 94 slashing delays from weeks hours. Despite these advancements, persist, including privacy risks, algorithmic opacity, infrastructural gaps low- middle-income countries. Explainable frameworks blockchain encryption are building clinician trust ensuring regulatory compliance. Future directions emphasize the convergence Internet Things (IoT) for diagnostics, as demonstrated AI-IoT forecasting dengue outbreaks 14 days advance. Personalized medicine, powered genomic wearable integration, further underscores tailor therapies, reducing cardiovascular events 25 %. Realizing this vision demands interdisciplinary collaboration, ethical governance, implementation bridge global health disparities. By harmonizing innovation accessibility, AI-enhanced emerges cornerstone proactive, patient-centered poised democratize diagnostics drive sustainable equity worldwide.

Язык: Английский

Процитировано

0