Aug 2026· Archives of Computational Methods in Engineering· 8 citations· ⚡ 1 influential· 98 references
TL;DR
This review provides a unified framework for understanding the evolution of volumetric medical imaging and offers actionable insights for researchers, clinicians, and industry practitioners, contributing to the development of reliable, interpretable, and clinically deployable next-generation medical AI systems.
Abstract
Volumetric medical imaging has redefined modern healthcare, enabling precise diagnosis, prognosis, and treatment planning. During the past decade, the field has undergone a paradigm shift from classical deep learning architectures to multimodal, agent-driven AI systems capable of uncovering rich volumetric biomarkers and utilizing heterogeneous data for predictive and generative modeling. Existing surveys are fragmented, focusing on specific models or tasks instead of offering a unified view of volumetric learning evolution. This study traces the evolution from classical models (Convolutional Neural Networks, Recurrent Neural Networks, and transformers) to generative approaches (Variational Autoencoders, Generative Adversarial Networks, and diffusion models) and finally to foundation models and AI-agents that enable advanced reasoning and adaptive clinical workflows. As the reported performance varies substantially across datasets, imaging modalities, and evaluation protocols, this review emphasizes methodological evolution, representative innovations, and practical implications rather than direct numerical ranking of competing architectures. For each paradigm, we critically assess methodological innovations, strengths, limitations, and comparative performance in segmentation, classification, detection, reconstruction, and report generation. Beyond synthesizing progress, we identify persistent challenges, including data scarcity, generalization between institutions, and clinical trustworthiness, and outline emerging frontiers in multimodal fusion, explainable AI, and human–AI collaboration. This review provides a unified framework for understanding the evolution of volumetric medical imaging and offers actionable insights for researchers, clinicians, and industry practitioners, contributing to the development of reliable, interpretable, and clinically deployable next-generation medical AI systems. To support further research, we provide a GitHub repository that includes popular 3D medical imaging datasets with recent 3D models in our shared GitHub repository (https://github.com/Owais-CodeHub/3D-Medical-Imaging-Review).
It is argued that LLM-based CDSS are not supported for routine autonomous use and require clinician supervision, and set out a research agenda centred on validation, governance and human-in-the-loop deployment.
A. Babu, A. J. Nehemiah, V. Jagadeep et al.· Frontiers in Digital Health· 0 citations
Traditional diagnostic methods in biomedicine are often constrained by subjectivity, misdiagnosis, and the inability to efficiently process large, complex, and multimodal datasets. Recent advances in deep learning and hybrid architectures have enabled the automated learning of discriminative representations from high‐dimensional biomedical data, supporting robust classification and integrative analysis across imaging, physiological signals, and clinical texts. This review synthesizes the current progress in applying deep learning to neuroimaging, cardiovascular disease diagnosis, functional connectivity analysis, and biomedical text mining, focusing on hybrid and ensemble strategies that combine complementary modeling strengths. These approaches show improved accuracy, scalability, and adaptability while extending situational awareness by incorporating patient‐generated and clinical textual data. Despite promising outcomes, challenges remain in data availability, computational efficiency, interpretability, and clinical integration. Future directions emphasize the development of explainable, multimodal, and generalizable frameworks capable of supporting precision medicine and advancing patient‐centered care.
Li-Feng Li, Ashfaque Khowaja, Yu-Cheng Song et al.· Medicine Advances· 0 citations
This review systematically explores deep learning‐driven advancements, focusing on their theoretical foundations, key technological breakthroughs and typical application scenarios, and systematically categorizes fusion architectures into data‐level, feature‐level, and decision‐level hierarchies across diverse modalities.
Haohan Wu, Yaping Xu, Zhiduo Zhang et al.· WIREs Data Mining and Knowle...· 0 citations
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
This paper addresses the problem of automated diagnosis of rare oncological diseases using medical imaging data with hybrid architectures combining Convolutional Neural Networks and Generative Adversarial Networks. The study focuses on a rigorous mathematical formalization of the entire pipeline, including data structure description, stratification procedures, preprocessing methods, and synthetic image generation to augment training datasets. The proposed approach generates additional synthetic images for each real positive example, thus compensating for class imbalance and improving classification accuracy under limited data conditions. The presented framework eliminates the ambiguity typical of purely verbal problem formulations, ensures reproducibility of results, and provides a foundation for optimizing hybrid architectures in few-shot learning scenarios. Future research directions include integrating the proposed approach into clinical workflows, investigating the impact of synthetic data parameters on diagnostic performance, and adapting the methodology to various types of rare pathologies.
A physics-Informed Convolutional Neural Network for sort of brain tumour categorization from MRI images and followed by its testing framework to improve consistency and healthcare organizational adoption of deep learning frameworks is proposed.
Ayat Muthana, Zainab Mohammad Habeeb· Al-Noor Journal of Engineeri...· 0 citations
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