Coronary artery disease (CAD) is a leading cause of mortality worldwide, demanding more precise diagnostic strategies. Traditional AI algorithms, like CNNs, RNNs, often fail to absorb these complex relational patterns within cardiovascular data. GNNs give an alternative because they can process such dynamic relationships. This work attempts to study GNNs for CAD progression modeling and diagnosis, including the integration of such models within cloud infrastructures for a scalable and real-time deployment. A systematic literature review was performed in accordance with PRISMA guidelines. The databases searched were PubMed, Web of Science, IEEE Xplore, and Google Scholar, yielding 259 articles. After applying inclusion criteria, 32 studies were selected. These were analyzed from the perspective of GNN architecture, CAD application area, strategies for cloud deployment, and diagnostic performance. GNN-based diagnostic models with accuracies of up to 96% and AUCs higher than 0.90 have been reported in the literature. Subsequent cloud deployment of these models allows real-time inference and easy integration into hospital systems, enabling federated learning of new models while preserving patient data privacy. Use cases include coronary imaging, ECG analysis, and behavioral risk profiling. GNNs, combined with cloud technologies, present a transformative approach for precision cardiology, enabling accurate and personalized CAD diagnostics. However, adoption in clinical settings requires further advancements in model explainability, privacy safeguards, and regulatory compliance. Future research should emphasize open CAD graph datasets, improve GNN interpretability, and validate these systems in real-world clinical environments

DILI presents symptoms like those in viral hepatitis in terms of elevated liver enzymes, jaundice, and liver dysfunction. These make it rather difficult to differentiate between DILI and viral hepatitis through traditional diagnostic methods. DILI is of profound relevance to global morbidity and mortality today. Thus, accurate diagnosis in terms of time and precision is essential. AI offers hope and promises to use improved diagnostics for personalized treatment strategies. After a systematic search on PubMed and Google Scholar, 933 studies were identified, and they concentrated on AI applications in differentiation regarding DILI and viral hepatitis. Only 55 studies were shortlisted for evaluation via a review process that covered diverse AI techniques deployed in diagnosis, including performance metrics. Models, AI have improved how DILI is diagnosed into a paradigm of distinctive biomarkers in a cross-section of clinical data. Machine learning algorithms using clinical data and imaging have very high accuracy in distinguishing DILI from viral hepatitis. It will improve early diagnosis, prognostic predictions, and novel therapeutic target identification by AI with multimodal-data cross-images, laboratory tests, and clinical history. Indeed, AI has the potential to be instrumental in enhancing the diagnosis of DILI and its differentiation from viral hepatitis. With the development of further advanced models, AI could even act as predictive compared to giving the facilities for drug-induced hepatotoxicity and thus enhance patient outcomes while reducing the cost-benefits for richer pharmaceutical processes.