Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{191938,
        author = {PALLAVI SANDEEP SALVE and SHRADDHA NAMDEV PUND and NISHA SONYABAPU YEWALE and ISHWARI SUNDARBAPU GUNJAL and PRACHI SACHIN MANGUDKAR},
        title = {AI IN HEALTHCARE FOR BRAIN TUMOUR PREDECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8262-8268},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191938},
        abstract = {Artificial Intelligence (AI) has emerged as a powerful and transformative technology in the healthcare domain, significantly improving the accuracy, speed, and efficiency of disease diagnosis. With the rapid growth of medical data generated from electronic health records, medical imaging systems, laboratory investigations, and wearable health devices, traditional diagnostic methods have become insufficient to handle large-scale and complex data. AI-powered healthcare diagnosis systems utilize advanced techniques such as machine learning, deep learning, natural language processing, and data analytics to analyse this vast amount of medical information and assist healthcare professionals in clinical decision-making.
These intelligent systems are capable of identifying hidden patterns in patient data, predicting disease risks, supporting early diagnosis, and reducing the chances of human error. AI-based diagnostic solutions have been widely applied in various medical fields including cancer detection, cardiovascular disease diagnosis, neurological disorder analysis, diabetes prediction, and infectious disease monitoring. This review paper provides a comprehensive analysis of existing AI-powered healthcare diagnosis systems, focusing on commonly used algorithms, system architectures, applications, advantages, and limitations reported in recent research studies.
Furthermore, this paper highlights major challenges such as data privacy, security concerns, model interpretability, ethical issues, and dependency on high-quality datasets. Research gaps related to real-time implementation, explainable AI, and integration with existing healthcare infrastructure are also discussed. Finally, the paper explores future research directions aimed at developing reliable, secure, and ethical AI-driven diagnostic systems that can enhance healthcare delivery. The primary objective of this review is to offer a structured and clear understanding of AI-powered healthcare diagnosis systems for students, researchers, and healthcare professionals.},
        keywords = {},
        month = {January},
        }