Recent advances in artificial intelligence (AI), particularly deep learning technology, have revolutionized medical imaging analysis. AI systems have demonstrated diagnostic capabilities matching or surpassing traditional methods and human expertise in specific radiological applications. While liver-focused AI diagnostic systems have shown promising results in multi-center validations, achieving accuracy levels comparable to senior radiologists, current AI models face critical challenges in real-world implementation. These challenges primarily stem from limited validation across diverse patient populations and varying imaging conditions, raising questions about their broader clinical applicability. While AI shows promise in hepatic malignancy detection and diagnosis, comprehensive validation through large-scale prospective trials is essential to establish real-world clinical effectiveness.