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AI in Neuroimaging and Brain Analysis
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Author(s): Aashish A. Gadgil (KLS Gogte Institute of Technology, Belagavi, India), P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), G. Sabeena Gnanaselvi (Sathyabama Institute of Science and Technology, India)and G. Malathi (Sri Akilandeswari Women's College, India)
Copyright: 2025
Pages: 28
Source title:
Transforming Neuropsychology and Cognitive Psychology With AI and Machine Learning
Source Author(s)/Editor(s): Rohit Bansal (Stanford Institute of Management and Technology, Australia), Tariq Maqableh (Charles Sturt University, Australia), Gunjan Shuklaa (SICA College, Indore, India), Fazla Rabby (Stanford Institute of Management and Technology, Australia)and Remya Lathabhavan (Indian Institute of Management, Bodh Gaya, India)
DOI: 10.4018/979-8-3693-9341-3.ch008
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Abstract
The integration of AI in neuroimaging offers unprecedented opportunities to enhance our understanding of the brain, improve diagnostic accuracy, and personalize treatment strategies for neurological disorders. This capability is particularly significant given the increasing volume and complexity of neuroimaging data generated by modalities such as MRI, CT, PET, and EEG. As AI algorithms evolve, they are not only enhancing image quality and acquisition processes but also aiding in the development of biomarkers for various neurological conditions. This capability can lead to earlier diagnosis and intervention, which is crucial in managing progressive conditions. Moreover, AI-driven approaches can streamline workflow processes in clinical settings, reducing the burden on radiologists and enabling more efficient patient management. Despite these opportunities, the incorporation of AI in neuroimaging also presents significant challenges. Data privacy and security are paramount concerns, especially when dealing with sensitive patient information.
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