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Artificial Intelligence and New Healthcare Technologies: A Global Perspective
ABSTRACT
Background:
The convergence of telemedicine and artificial intelligence (AI) represents a transformative force in healthcare delivery with potential to both reduce and exacerbate disparities. Despite accelerated adoption during the COVID-19 pandemic, a comprehensive assessment of AI-enhanced telemedicine across healthcare dimensions remains incomplete.
Objective:
To examine the intersection of AI and telemedicine at patient, organizational, and population levels, highlighting opportunities, challenges, and emerging applications while identifying strategies to ensure equitable implementation.
Methods:
This narrative review synthesizes evidence from peer-reviewed literature, clinical trials, and commercial applications to evaluate AI-telemedicine integration. We analyze diagnostic applications, monitoring technologies, and decision support systems at the individual patient level; operational implementations and workflow optimizations at the organizational level; and predictive modeling, resource allocation, and public health surveillance at the population level.
Results:
AI applications in telemedicine demonstrate substantial promise, with diagnostic algorithms approaching or exceeding expert-level performance in specialties including dermatology (AUC >0.90), ophthalmology (sensitivity >90%), and cardiology. Organizations implementing AI-enhanced telehealth report improved operational efficiency and resource utilization. Population-level applications show particular utility in disease surveillance, pandemic response, and addressing healthcare disparities, though significant challenges remain in algorithm bias, data privacy, and equitable access. The emerging landscape of foundation models offers improved generalizability across diverse populations but requires rigorous validation to prevent amplification of existing inequities.
Conclusions:
AI-enhanced telemedicine can potentially transform healthcare delivery by increasing access to specialized expertise, optimizing resource allocation, and enabling personalized care. However, realizing these benefits requires addressing interoperability challenges, mitigating algorithmic bias, ensuring data privacy, and developing regulatory frameworks that balance innovation with patient safety. Future research should focus on prospective validation of AI applications in diverse populations and care settings to ensure equitable implementation.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.