Title : Revolutionizing alzheimer's early detection: bridging gaps with multi-modal imaging, AI innovations, and ethical insights
Abstract:
Background: Early detection of Alzheimer's disease (AD) is crucial for timely intervention and improved patient outcomes. Despite advancements in neuroimaging and AI, significant gaps remain in the early diagnosis of AD.
Aims: This session aims to explore the integration of multi-modal imaging and AI innovations in enhancing early detection of AD, while addressing ethical considerations and limitations.
Methods: We will review recent advancements in MRI, PET, and fMRI techniques, alongside AI algorithms for analyzing neuroimaging data. Ethical implications of early diagnosis and AI use in healthcare will be discussed.
Results: Preliminary findings suggest that combining multi-modal imaging with AI can improve diagnostic accuracy and reduce false positives. Ethical challenges include data privacy, informed consent, and equitable access to advanced diagnostics.
Conclusions: Integrating multi-modal imaging and AI holds promise for revolutionizing AD early detection. Addressing ethical concerns and refining AI algorithms are essential for responsible implementation and maximizing patient benefits.
Keywords: Alzheimer's disease, early detection, multi-modal imaging, AI innovations, ethical considerations
