Advances in AI and machine learning for predictive medicine have transformed the field of omics, enabling more accurate predictive modeling in precision medicine. The abundance of omics data presents challenges in analysis and interpretation, but deep learning (DL), particularly convolutional neural networks (CNNs), offers a promising solution. By converting tabular omics data into image-like representations using methods like Deeplnsight, CNNs can effectively capture latent features, enhancing predictive power and enabling transfer learning. This approach not only improves performance but also reduces computational time. However, integrating CNNs into omics data analysis faces challenges such as model interpretability, data heterogeneity, and data size. Addressing these requires interdisciplinary collaboration between ML experts, bioinformatics researchers, biologists, and medical doctors. DeepInsight and DeepFeature are key methodologies in this context. DeepInsight transforms tabular data into image-like formats, facilitating CNN analysis, while DeepFeature enhances model interpretability by highlighting key features using class activation maps (CAMs). DeepInsight-3D extends this approach to multi-omics data, integrating information across different omic types into a unified 3D space, enabling holistic analysis. This method has shown improved performance in drug response prediction, achieving 72% accuracy. scDeepInsight applies these techniques to single-cell RNA sequencing (scRNA-seq) data, enabling precise cell-type identification and discovery of new cell types and marker genes. Despite these advancements, challenges remain, including ensuring model interpretability, handling data heterogeneity, and preventing overfitting. Future directions include enhancing model generalizability, improving computational efficiency, and integrating domain-specific knowledge. The integration of DL with biology holds promise for personalized medicine, enabling tailored therapeutic strategies and deeper insights into disease mechanisms. Interdisciplinary collaboration is crucial to fully harness the potential of these methodologies in advancing omics data analysis and interpretation.Advances in AI and machine learning for predictive medicine have transformed the field of omics, enabling more accurate predictive modeling in precision medicine. The abundance of omics data presents challenges in analysis and interpretation, but deep learning (DL), particularly convolutional neural networks (CNNs), offers a promising solution. By converting tabular omics data into image-like representations using methods like Deeplnsight, CNNs can effectively capture latent features, enhancing predictive power and enabling transfer learning. This approach not only improves performance but also reduces computational time. However, integrating CNNs into omics data analysis faces challenges such as model interpretability, data heterogeneity, and data size. Addressing these requires interdisciplinary collaboration between ML experts, bioinformatics researchers, biologists, and medical doctors. DeepInsight and DeepFeature are key methodologies in this context. DeepInsight transforms tabular data into image-like formats, facilitating CNN analysis, while DeepFeature enhances model interpretability by highlighting key features using class activation maps (CAMs). DeepInsight-3D extends this approach to multi-omics data, integrating information across different omic types into a unified 3D space, enabling holistic analysis. This method has shown improved performance in drug response prediction, achieving 72% accuracy. scDeepInsight applies these techniques to single-cell RNA sequencing (scRNA-seq) data, enabling precise cell-type identification and discovery of new cell types and marker genes. Despite these advancements, challenges remain, including ensuring model interpretability, handling data heterogeneity, and preventing overfitting. Future directions include enhancing model generalizability, improving computational efficiency, and integrating domain-specific knowledge. The integration of DL with biology holds promise for personalized medicine, enabling tailored therapeutic strategies and deeper insights into disease mechanisms. Interdisciplinary collaboration is crucial to fully harness the potential of these methodologies in advancing omics data analysis and interpretation.