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This study investigates various human-AI collaboration protocols~-~traditional, inhibition, displacement, and replacement~-~across multiple medical settings, including radiological imaging, ECG, and endoscopy. We introduce a novel framework that includes a choice nomogram and qualitative assessment tool, designed to optimize both decision accuracy and socio-technical impacts. Our findings reveal that the displacement protocol consistently outperformed others in several contexts, achieving 87% accuracy in MRI analysis, 89% in x-ray reading and 85% in endoscopy; conversely, the traditional protocol was most effective only in ECG analysis, with 82% accuracy. 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