Deep Learning AI and future hybrid algorithm development for the prediction of future dementia developing risk

Abstract

<p class="first" dir="auto" id="d7556751e71">While the potential of AI and simulation in healthcare has been widely recognized for many years, their integration into clinical practice remains limited. This lack of widespread adoption in neurological healthcare is not primarily due to a shortage of advanced computer technologies. Instead, it can be attributed to several key factors: i)Interdisciplinary Conceptual Understanding: One major challenge is the absence of a comprehensive interdisciplinary understanding of algorithms related to brain diagnostics. Effective AI and simulation tools require a deep integration of medical knowledge with computer science expertise: i) Another critical aspect is the availability of extensive neurological data. There is a need for robust infrastructure for storing and managing large sets of sensitive neurological data, ensuring privacy and security. ii) Transparency and Standardization. To gain trust and acceptance in the medical community, standardized internal revision procedures and transparency standards are essential. This includes clear documentation of how AI and simulation algorithms arrive at their conclusions. iii) Real-World Evidence: Demonstrating the real-world effectiveness of AI and simulation tools is crucial. Decisionmakers require solid evidence that these technologies can improve patient outcomes and reduce healthcare costs. Addressing these challenges is vital for the cost-efficient implementation of personalized diagnostics and treatment in neurological healthcare through group-based AI solutions and individualized simulation technologies. The European Commission's "White Paper on Artificial Intelligence" in 2020 acknowledges the transformative potential of merging AI and simulation, often referred to as 'hybriding,' for the future of personalized healthcare.mHowever, despite this recognition, the practical integration of these approaches into clinical settings remains underdeveloped. To unlock the transformative potential of hybrid AI and simulation technologies in personalized neurology, there is an urgent need for new cross-sector knowledge and educational initiatives. These initiatives should focus on overcoming the challenges related to interdisciplinary collaboration, data management, transparency standards, and real-world applications.

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