Artificial Intelligence in Science and Technology: A Critical Narrative Review of Applications, Evidence, Risks and Emerging Directions

Abstract

Artificial intelligence (AI) has become a general-purpose set of computational methods that is increasingly embedded in scientific research and technological systems. This critical narrative review evaluates the principal roles of AI across scientific discovery, medicine, environmental and space science, manufacturing, software engineering, cybersecurity and communication networks, while distinguishing demonstrated capabilities from claims that remain context dependent. The review also examines conceptual differences between task-bounded AI and human intelligence, and considers human–AI collaboration as a practical design problem rather than a contest between interchangeable forms of intelligence. Literature was selected through transparent live scholarly searching and verification, with an emphasis on peer-reviewed evidence, primary studies for prominent performance claims and authoritative governance sources. The evidence indicates that AI is especially useful where large, high-dimensional or multimodal data must be classified, modelled or searched, and where repeated decisions can be evaluated against explicit performance criteria. Examples include protein-structure prediction, anomaly detection, remote-sensing analysis, autonomous planetary navigation, smart manufacturing and cyber-threat analysis. Nevertheless, high benchmark performance does not automatically establish clinical utility, causal understanding, robustness under distribution shift, safety or equitable deployment. Major cross-cutting limitations include data quality, representativeness, opaque decision processes, evaluation leakage, infrastructure requirements, environmental cost, cybersecurity vulnerabilities and uneven institutional capacity. Responsible integration therefore requires task-specific validation, human oversight, traceability, monitoring and governance proportional to potential harm. Future progress will depend less on simply increasing model scale than on improving scientific validity, causal and mechanistic integration, reproducibility, context-sensitive evaluation and access in lower-resource settings. AI is best understood as an enabling technology whose scientific and social value depends on the quality of the evidence, institutions and human judgement surrounding its use.