Abstract
Brain tumors pose a major challenge in neuro-oncology due to their high mortality rates and complex diagnosis. This review summarizes recent advances in using artificial intelligence (AI), particularly deep learning, in conjunction with thermal imaging and simulated thermal mapping for brain tumor detection. AI methods such as convolutional neural networks (CNNs), hybrid architectures, and bioheat transfer models, including the Pennes equation, are evaluated to determine how temperature variations, tumor biology, and image preprocessing influence malignancy classification. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), provide detailed structural information but are often costly, invasive, and limited in their ability to capture physiological data. Recent studies indicate that integrating AI with thermal imaging, either through direct infrared thermography or simulated thermal maps derived from MRI, enables non-invasive, physiology-aware diagnosis. The review examines current approaches to thermal data preprocessing, simulation, deep learning-based tumor segmentation, and malignancy prediction, as well as key evaluation metrics, model interpretability tools, and recent performance outcomes. Despite ongoing progress, challenges remain, including limited availability of multimodal datasets, variability in thermal signatures, and the need for clinical validation. Future research directions include large-scale data collection, advanced thermal modeling, multimodal fusion frameworks, and the development of explainable AI tools that meet clinical standards. In resource-limited settings, AI-powered thermal imaging may serve as a valuable supplement to traditional diagnostics, offering safer, more precise, and more accessible brain tumor detection. This technology has the potential to improve patient outcomes and transform neuro-oncology practices by integrating anatomical and functional insights. This review critically evaluates current evidence and identifies the challenges that must be addressed to facilitate the translation of promising research into clinical practice.