AI’s performance in clinical settings can vary significantly compared to its efficacy reported in the literature. While studies typically showcase AI’s potential to enhance diagnostic accuracy and streamline workflows, real-world applications reveal challenges that may impact outcomes. Factors such as data quality, integration with existing systems, and human oversight play crucial roles in AI performance. Additionally, the variability in clinical environments and practices can affect AI’s reliability and effectiveness. It is essential for healthcare professionals to critically evaluate AI technologies, considering both their potential benefits and limitations in practical scenarios. As AI continues to evolve, ongoing research and real-world evaluations are necessary to ensure that these systems can operate effectively in diverse clinical contexts. Proper training and interdisciplinary collaboration can help bridge the gap between theoretical performance and practical application, ultimately leading to better patient outcomes and enhanced healthcare delivery.
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