Volume 3,Issue 5
Deep Fusion and Efficiency Enhancement of AI Intelligent Diagnostic System in Medical Image Processing
To explore the deep fusion mode of AI intelligent diagnostic system in medical image processing and its role in improving diagnostic efficiency. Method: A retrospective analysis was conducted on the data of 300 patients who underwent CT, MRI, or X-ray examinations in our hospital from January 2023 to January 2024. Among them, 150 patients were diagnosed using traditional medical image analysis methods (traditional group), and 150 patients were diagnosed using an analysis process integrated with an AI intelligent diagnostic system (AI group). Compare the diagnostic accuracy, diagnostic time, lesion detection rate, and missed diagnosis rate between two groups. Result: The diagnostic accuracy of the AI group was 94.67%, significantly higher than the traditional group's 86.67% (P<0.05); The average diagnosis time for the AI group was 12.3 minutes, which was significantly shorter than the traditional group's 21.5 minutes (P<0.05); The lesion detection rate in the AI group reached 92.37%, which was higher than the 83.59% in the traditional group (P<0.05); The missed diagnosis rate in the AI group was 3.34%, which was lower than the traditional group's 10.71% (P<0.05). Conclusion: The deep integration of AI intelligent diagnostic systems and medical image processing can significantly improve diagnostic efficiency, optimize clinical workflow, provide strong support for precision medicine, and have broad application prospects.
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