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Artificial Intelligence in In Vitro Diagnostics (IVD): A Comprehensive Review of the New Frontier

2026·0 Zitationen·IEEE AccessOpen Access
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Abstract

The healthcare industry is undergoing a dual transformation driven by digital innovation and sustainability. This paper provides a comprehensive review of the pivotal role of Artificial Intelligence in In Vitro Diagnostics (AI-in-IVD) as the primary enabler of this shift. Through a systematic review of academic literature, market reports, and regulatory documents, we analyze the technological maturity, disruptive potential, and market trajectory of AI across key medical domains. The results indicate that while all areas of medical AI are growing, the IVD sector demonstrates superior potential, with market forecasts projecting a Compound Annual Growth Rate (CAGR) of over 14.8% to 20.37% through 2034, driven by its alignment with both digital and sustainable transformations. Our synthesis identifies four key AI-driven frontiers in IVD: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liquid biopsies, digital pathology, multi-omics integration</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point-of-care testing</i>. We demonstrate how these innovations uniquely align with the dual transformation framework by advancing P4 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predictive, Preventive, Personalized, Participatory</i>) medicine while simultaneously promoting healthcare sustainability through resource optimization and decarbonization. The review concludes that the AI-in-IVD sector represents the most strategic frontier for innovation and investment, offering a roadmap for building resilient and equitable healthcare systems, with specific implications discussed for emerging economies with rapidly growing AI ecosystems, such as Vietnam.

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Artificial Intelligence in Healthcare and EducationAI in cancer detectionMachine Learning in Healthcare
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