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Despite significant advancements in AI medical technology, its application in clinical settings is hindered by a 'translatability gap,' delaying real-world impact.

Discrepancy Between AI Medical Progress and Clinical Application

Artificial intelligence (AI) technology has shown revolutionary potential in healthcare, including improving diagnostic accuracy and developing new treatments. However, many AI-based medical technologies, despite their success in research settings, face challenges when being applied in real-world clinical environments. This phenomenon is referred to as the 'translatability gap,' where research findings fail to translate into practical healthcare services.

Data Mismatch and Regulatory Hurdles

Key factors hindering the clinical application of AI medical technologies include the discrepancy between data used in the research phase and actual patient data. Research often relies on refined data from controlled environments, whereas real-world clinical data exhibit greater diversity and complexity. Furthermore, stringent regulatory approval processes and issues of liability for new AI medical devices also contribute to the delayed adoption of these technologies.

*Source: the-scientist.com (2026-03-18)*

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