An Impactal study on Artificial Neural Networks (ANNs) in the Context of Artificial Intelligence

Abstract: Artificial neural networks (ANNs) have substantially influenced artificial intelligence by enabling computational systems to learn representations from images, language, behavioural sequences, and other complex data. This paper presents a selective narrative review of their conceptual development, applications, evaluation requirements, and societal implications. It synthesises foundational and application-oriented publications concerning computer vision, natural language processing, healthcare image classification, financial prediction, cybersecurity, and reinforcement learning. The review uses a qualitative comparative framework to examine the task addressed, the evidence reported, and the conditions limiting interpretation. The reviewed literature illustrates the value of learned representations and task-specific architectures, while also showing why benchmark performance cannot alone establish operational reliability or social benefit. Cross-cutting concerns include data quality, distribution shift, interpretability, subgroup disparities, adversarial vulnerability, privacy, and computational requirements. The paper proposes a practical evaluation framework connecting task suitability, data provenance, appropriate baselines, external validation, human oversight, and monitoring after deployment. No new model training, expert interviews, public-opinion survey, or pooled statistical analysis is reported. The synthesis concludes that the contribution of ANNs should be assessed through performance under relevant operating conditions and the consequences of their use, rather than through accuracy alone. Keywords: artificial neural networks; artificial intelligence; deep learning; representation learning; model evaluation; responsible AI.