This article develops an original, publication-ready research study based strictly on the supplied references and examines two rapidly advancing but structurally comparable domains of intelligent decision-making: lung cancer detection from computed tomography imagery and electric vehicle integration into modern power systems. Although these domains differ in application, both represent high-stakes environments in which data-driven systems must operate under uncertainty, manage complex patterns, and support decisions with substantial human, societal, and infrastructural consequences. The medical studies in the provided corpus emphasize convolutional neural networks, transfer learning, hybrid deep learning methods, attention-based false-positive reduction, hyperparameter optimization, and validated predictive models for lung cancer screening, detection, segmentation, and risk prediction (Al-Huseiny & Sajit, 2021; Sun et al., 2021; Mikhael et al., 2023; Musthafa et al., 2024; Abe et al., 2025). The energy studies focus on reinforcement learning, deep reinforcement learning, vehicle-to-grid scheduling, voltage support, EV fleet services, charging coordination, renewable integration, and intelligent charging station design (Sortomme & El-Sharkawi, 2011; Richardson et al., 2012; Chen et al., 2022; Papadopoulos et al., 2023; Liu et al., 2023; Mohan et al., 2025).

Using a qualitative integrative methodology, this article synthesizes the conceptual, methodological, and operational patterns across these sources. The results reveal four dominant findings. First, both fields are moving from rule-based or isolated optimization toward adaptive, data-driven intelligence. Second, performance gains increasingly depend on architecture refinement, feature engineering, transfer learning, and optimization rather than on simple algorithm substitution alone. Third, deployment success in both domains depends not only on accuracy or efficiency, but also on reliability, interpretability, and management of false positives, uncertainty, and resource constraints. Fourth, the comparative reading of the literature suggests that health and energy systems are converging around a shared paradigm of intelligent socio-technical infrastructure in which machine learning functions as a coordinating rather than merely predictive tool. The discussion interprets these patterns, identifies limitations of current approaches, and proposes a future research agenda centered on trustworthy, interoperable, and domain-sensitive intelligence. The study concludes that the most meaningful future advances will come from frameworks that combine technical performance with system-level accountability in both medical imaging and EV-grid ecosystems.