Feature Dimensions of Artificial Intelligences Challenges and Techniques - A Survey

Abstract

Artificial Intelligence (AI) is rapidly transforming sectors such as healthcare, education, and public services, contributing new solutions that advance efficiency, management, and overall outcomes. However, despite its vast potential, AI adoption faces numerous challenges, including ethical concerns (e.g., algorithmic bias), data privacy issues, and integration difficulties with legacy systems. This paper provides a comprehensive survey of AI applications across these sectors, analyzing over 60 recent studies from 2019 to 2024 after the PRISMA methodology. The study identifies key factors influencing successful AI implementation by highlighting sector-specific challenges and shared barriers. The PRISMA framework was applied for systematic paper selection, including inclusion and exclusion criteria, screening, and data extraction, ensuring that only relevant, high-quality studies were reviewed. These experimental results reveal that the AI models consistently outperform state-of-the-art techniques in critical domains, including medical diagnosis, personalised education, and public service optimisation. This hybrid approach, which combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs), outperforms existing models by addressing challenges in data preprocessing, model architecture, and hyperparameter optimisation. Additionally, the paper explores the future of AI and its integration with up-and-coming technologies such as quantum computing, blockchain, and the metaverse while providing strategies to overcome legal, cultural, and infrastructural barriers to AI adoption. These findings offer actionable insights for researchers, practitioners, and policymakers, emphasising the need for both technical innovation and ethical considerations in AI growth and execution.

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