The application of AI in the field of medicine, particularly in interpreting medical imaging, has the potential to improve efficiencies and precision in diagnostics. Despite the possible benefits of AI, the practical application of its models in clinic-based settings is hindered by multiple barriers. In this work, we analyze the entire YOLO family from version 8 to 12 on three medical imaging datasets: HAM10000 (skin lesions), MURA (musculoskeletal X-rays), and Blood Cell Detection (microscopic images). Our findings reveal a myriad of problems such as class imbalance, spatially varying object scale, model overconfidence, and others that hinder performance in bolstering real world where AI-assisted diagnostics should function. With a lack of specialized training, clinical accuracy is commonly plummeted as tested on the laser-focused circumstances. However, this trend is not universal. In fact, YOLOv8 seems the best among all others: Newer versions with more advanced algorithms trade away precision for speed. To address deployment challenges, we recommend aggressive data augmentation, advanced loss function design, and extreme training. The results illuminate critical gaps in AI training frameworks that enforce reliability, thereby improving the safety and efficacy of AI deployment in medicine.