Artificial intelligence based techniques for brain tumor analysis: A systematic review.

No prior work had resolved the discrepancy between high-performing computational models and their absence in real-world clinical settings. Researchers often struggle to translate experimental diagnostic tools into standard hospital workflows. While abnormal cell growth detection remains a priority, the current landscape of automated analysis lacks standardized implementation. This uncertainty drove the need to evaluate how various automated diagnostic frameworks perform across diverse medical datasets. Prior research has shown that early detection significantly improves patient survival rates while lowering overall healthcare expenditures. Despite these benefits, the transition from academic prototypes to bedside utility remains stalled. This gap motivated a comprehensive assessment of existing literature to pinpoint why these technologies have not achieved widespread adoption. Understanding the limitations of current diagnostic models is necessary for advancing future medical imaging solutions.

The aim of this study is to provide a systematic review of computational techniques used for identifying and analyzing brain tumors. Researchers sought to evaluate the current state of automated diagnostic tools in medical imaging. The project addresses the challenge of translating experimental algorithmic successes into practical, hospital-based applications. By examining existing literature, the authors intended to identify common methods, datasets, and performance metrics. They also aimed to uncover significant gaps in the current research, such as the lack of explainable models. This investigation was motivated by the need to understand why automated systems have not yet been fully adopted by clinicians. The authors provide a structured overview of the field to guide future development efforts. This work serves as a baseline for assessing the maturity of automated diagnostic technologies in oncology.

The review approach followed the established Kitchenham and Charters protocol to ensure rigorous selection of relevant academic literature. Investigators searched the IEEE Xplore and ACM bibliographic databases to capture studies published between 2013 and 2024. Initial screening yielded 3,950 potential documents, which were subsequently refined using specific inclusion and exclusion criteria. This filtering process resulted in a final set of 101 papers for detailed evaluation. The team defined seven distinct research questions to categorize methods, datasets, and performance metrics. They also examined the prevalence of explainable approaches within the selected studies. This systematic design allowed for a comprehensive mapping of the current technological landscape. The methodology emphasizes transparency in how evidence was gathered and synthesized from the broader field.

Key findings from the literature reveal that 101 papers met the criteria for inclusion out of 3,950 initially identified sources. The analysis confirms that despite extensive research into segmentation and classification, no automated methods have achieved full clinical integration. The authors identified a significant research gap regarding weakly-supervised segmentation techniques, which remain largely unaddressed in the current body of work. Furthermore, only four articles focused on explainable approaches, indicating a major deficiency in model transparency. The review highlights that most existing studies prioritize performance metrics over the interpretability required for medical decision-making. These results demonstrate that while technical progress is evident, the field lacks the necessary focus on clinical deployment and model explainability. The data suggests an urgent need for research that addresses these specific limitations to improve diagnostic utility. The findings underscore a disconnect between academic performance and the practical requirements of oncology departments.

The authors suggest that current computational diagnostic models have not yet achieved full integration into standard clinical workflows. Synthesis and implications indicate that while segmentation and classification tasks receive significant attention, practical application remains limited. The review highlights a notable scarcity of research concerning weakly-supervised segmentation techniques for brain lesions. This finding suggests that current methodologies may struggle with datasets lacking precise, pixel-level annotations. The authors emphasize that only a small fraction of studies prioritize explainable models, which are vital for medical transparency. Greater focus on interpretable decision-making processes could bridge the trust gap between clinicians and automated systems. Future investigations should prioritize these transparent frameworks to enhance diagnostic reliability in oncology. The synthesis confirms that bridging the divide between experimental success and clinical utility requires addressing these specific technical and interpretative shortcomings.