Comparative analysis of Hybrid and Classic AI Models in Cancer Detection

The current review is a synthesis of the research on the topic "Comparison of hybrid AI models with classic modelsin detecting cancer in healthcare systems" in order to solve the problems of diagnostic accuracy, robustness,interpretability and clinical integration. The purpose of the review was to determine diagnostic performance,compare hybrid and classic methods of benchmarking across cancer types and modalities, feature fusion methods,interpretability, and validation methods. A systematic review of the literature published by the middle of 2024 hasbeen performed, and models of deep learning with classical machine learning and different types of cancer werereviewed such as breast, lung, and lymphoma. Results show that hybrid AI models are always more effective androbust in diagnosing than classic models, and frequently above 95 percent accurate through using multimodal dataand innovative feature fusion. Explainable AI approaches use more transparency, but are not yet well-developed tobe used on a large scale in clinical settings. Computational requirements are a problem to scalability and validationpractices are too diverse to be generalized, making it difficult to do so. The synthesis highlights the enhancedperformance and multimodal integration of hybrid models on the background of pervasive interpretability andstandardization loopholes. These findings provide a perspective on the potential of hybrid AI in enhance accuracyand resilience of cancer detection in healthcare systems and the necessity of standardized evaluation systems andimproved understandability to facilitate clinical translation and real-world use.