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Brain tumors must be detected accurately and promptly to improve patient outcomes and plan effective treatments. Recently used advanced technologies such as artificial intelligence (AI) and machine learning (ML) have increased interest in applying AI to detect brain tumors. However, concerns have emerged regarding the reliability and transparency of AI models in medical settings, as their decision-making processes are often opaque and difficult to interpret. This research is unique in its focus on explainability in AI-based brain tumor detection, prioritizing confidence, safety, and clinical adoption over mere accuracy. It gives a thorough overview of XAI methodologies, problems, and uses, linking scientific advances to the needs of real-world healthcare. XAI is a sub-section of artificial intelligence that seeks to solve this problem by offering understandable and straightforward and providing explanations for the choices made by AI representations. Applications such as healthcare, where the interpretability of AI models is essential for guaranteeing patient safety and fostering confidence between medical professionals and AI systems, have seen the introduction of XAI-based procedures. This paper reviews recent advancements in XAI-based brain tumor detection, focusing on methods that provide justifications for AI model predictions. The study highlights the advantages of XAI in improving patient outcomes and supporting medical decision-making. The findings reveal that ResNet 18 performed better, with 94% training accuracy, 96.86% testing accuracy, low loss (0.012), and a rapid time\n                    <jats:inline-formula>\n                      <jats:tex-math>$$(\\sim 6\\text {s})$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    . ResNet 50 was a little slower\n                    <jats:inline-formula>\n                      <jats:tex-math>$$(\\sim 13\\text {s})$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    but stable, with 92.86% test accuracy. DenseNet121 (Adam W) achieved the highest accuracy at 97.71%, but it was not consistent across all optimizers. ViT-GRU also got 97% accuracy with very little loss (0.008), although it took a long time to compute (around 49\u00a0s). On the other hand, VGG models (around 94% test accuracy) and MobileNetV2 (loss up to 6.024) were less reliable, even though they trained faster. Additionally, it explores various opportunities, challenges, and clinical applications. Based on these findings, this research offers a comprehensive analysis of XAI-based brain tumor detection and encourages further investigation in specific areas.\n                  <\/jats:p>","DOI":"10.1007\/s10462-025-11410-8","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T05:22:52Z","timestamp":1763443372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploring the potential of explainable AI in brain tumor detection and classification: a systematic review"],"prefix":"10.1007","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8223-5174","authenticated-orcid":false,"given":"Lincy Annet","family":"Abraham","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9806-9473","authenticated-orcid":false,"given":"Gopinath","family":"Palanisamy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9277-465X","authenticated-orcid":false,"given":"Goutham","family":"Veerapu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-6729","authenticated-orcid":false,"given":"J. 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