"Impact-X: A Causally-Grounded Interpretable Multimodal Deep Learning Framework for Transparent Early Disease Detection Using Imaging, Clinical, and Genomic Data." (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Recent developments in multimodal deep learning have brought great progress to early disease detection; yet, wide-scale implementation of such models in clinics is hindered by the inherently inscrutable reasoning of existing methods. Current frameworks often employ post-hoc explanations that are not cross-modal consistent and are unable to disambiguate between causality and correlation, compromising both clinician trust and patient safety. In order to resolve these key issues, we introduce IMPACT-X, a novel Causally-Grounded Interpretable Multimodal Deep Learning Framework. IMPACT-X fuses mul-tiple heterogeneous modalities—medical imaging with Vision Transformers, medical records with Tabular Transformers, and genetic sequences with Graph Neural Networks—into a single and interpretable model.Our framework includes a novel Causal Multimodal Fusion Layer (CMFL) which leverages cross-modal attention alignment in order to align the representation in a dynamic manner. Fur-thermore, an SCM module with DAG learning capabilities helps identify latent confounders and ensures the causally-consistent nature of the predictions. An uncertainty-aware decision-making layer estimates epistemic uncertainty through Monte Carlo Dropout in order to produce confidence scores. A unique cross-modal interpretability alignment loss function ensures coherent explanations across multiple modalities. The experimental results show that IMPACT-X achieves an SOTA performance with AUC-ROC score of 0.94, beating the best black-box baseline by 5.2%. Quantitative evaluation shows that IMPACT-X is 40% better in terms of faithfulness than traditional attention mechanism-based explanation approaches. A qualitative study with practicing medical professionals shows the benefits of causality-grounded predictions by increasing the level of physician trust in the system output. With its combination of high prediction accuracy and causal interpretability, IMPACT-X can pave the way for the development of a regulatory compliant and interpretable paradigm of medical AI that can safely be implemented in clinics, while enabling more accurate personalized medicine practices.Index Terms—Multimodal Deep Learning; Causal Inference; Interpretability; Early Disease Detection; Clinical Decision Sup-port; Genomic Integration