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

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.