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Artificial intelligence (AI) in point-of-care testing.

Tahir S Pillay1, Adil I Khan2, Sedef Yenice3

  • 1Department of Chemical Pathology, Faculty of Health Sciences and National Health Laboratory Service, Tshwane Academic Division, University of Pretoria, Pretoria, South Africa; Division of Chemical Pathology, Department of Pathology, University of Cape Town, Cape Town, South Africa.

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PubMed
Summary

Artificial intelligence (AI) enhances point-of-care testing (POCT) by improving diagnostic accuracy and efficiency. AI integration addresses healthcare challenges, aiming for equitable access and personalized medicine globally.

Keywords:
Artificial intelligenceDiagnostic accuracyElectronic health recordsMachine learningPersonalized medicinePoint-of-care testing

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Area of Science:

  • Healthcare technology
  • Medical diagnostics
  • Artificial intelligence

Background:

  • Point-of-care testing (POCT) offers rapid diagnostics but faces challenges in accuracy, integration, and resource limitations.
  • Artificial intelligence (AI) technologies, including machine learning and deep learning, present solutions to these POCT limitations.

Purpose of the Study:

  • To explore the transformative impact of AI integration in POCT.
  • To highlight AI's role in enhancing diagnostic accuracy, workflow efficiency, and accessibility in healthcare.
  • To address challenges and outline future directions for AI-enhanced POCT.

Main Methods:

  • Review of AI applications in POCT, including machine learning for disease detection (e.g., malaria) and predictive analytics for device management.
  • Analysis of AI-driven decision support systems for antimicrobial stewardship and portable AI devices for anemia screening.
  • Examination of emerging AI trends like Explainable AI, blockchain, and AI-IoT convergence for predictive diagnostics and personalized medicine.

Main Results:

  • AI improves malaria detection sensitivity to 95% and reduces device downtime by 20%.
  • AI systems curtail antibiotic misuse by 40% and enable rapid anemia screening with 94% accuracy.
  • AI-IoT systems forecast disease outbreaks 14 days in advance, and AI integration in personalized medicine reduces cardiovascular events by 25%.

Conclusions:

  • AI-enhanced POCT significantly improves diagnostic capabilities, efficiency, and accessibility, particularly in resource-limited settings.
  • Addressing challenges like data privacy and algorithmic opacity through Explainable AI and blockchain is crucial for trust and compliance.
  • The convergence of AI with IoT and blockchain promises a future of proactive, personalized, and equitable healthcare, driving global health equity.