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@article{185331,
        author = {Mr. Suraj S. Bhoite and Ms. Swapnali K. Londhe and Ms. Deepali Narwade},
        title = {Multimodal Artificial Intelligence for Brain Tumor Prediction: Bridging Accuracy and Explainability},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {923-925},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185331},
        abstract = {Brain tumor diagnosis using medical imaging has gained significant support from Artificial Intelligence (AI), particularly deep learning models. However, existing approaches often focus on single imaging modalities, limiting diagnostic reliability and interpretability. This paper proposes a multimodal AI framework that integrates MRI sequences, radiomic features, and clinical metadata for brain tumor prediction. To address concerns of transparency and trust, Explainable AI (XAI) techniques are incorporated to visualize and justify model predictions. Experiments on publicly available MRI datasets demonstrate improved diagnostic accuracy and higher clinician acceptance compared to single-modality baselines. The findings highlight the importance of multimodal learning in capturing tumor heterogeneity and underscore the role of XAI in ensuring accountability in clinical practice.},
        keywords = {Multimodal AI, Brain Tumor Detection, MRI, Radiomics, Explainable AI, Medical Imaging.},
        month = {October},
        }