Table 3 AGI-related review studies.
Author(s) | Research focus | Research gaps |
|---|---|---|
Bécue et al.62 | Explores AGI-driven innovation through the alignment of AI maturity, manufacturing strategies, and innovation capacity, emphasizing AGI’s role in decision-making and complex problem-solving in Industry 5.0 | Alignment of AI maturity with innovation metrics |
Yue and Shyu63 | Leverages AGI in the creation of intelligence fusion networks for proactive crisis management, incorporating principles of multisourced data integration, situational awareness, and decision-making | Scalability of AGI-driven intelligence fusion networks |
Li et al.18 | Utilizes AGI principles such as multimodal learning, domain-specific knowledge extraction, and operational optimization for addressing complex challenges in the geoenergy sector | Domain-specific knowledge for AGI application |
Wu42 | Examines AGI’s impact on redefining professional roles and skills, focusing on AGI-human collaboration, ethical reasoning, and adaptability in an AGI-driven information environment | Skillset adaptation for AGI-driven environments |
Chiroma et al.64 | Focuses on AGI-related applications in IoMT, including explainable AI for healthcare decision-making, and predictive analytics for real-time health monitoring systems | Addressing security and privacy in IoMT |
McLean et al.37 | Analyzes AGI-related risks such as goal misalignment, autonomous decision-making, and the existential threats posed by AGI, proposing governance frameworks to mitigate such risks | Lack of standard AGI terminology and definitions |
Reviews AGI-driven large vision models and visual prompt engineering, emphasizing AGI’s capability to adapt prompts for generalizable and context-aware visual tasks | Designing efficient visual prompts for AGI systems | |
Daase and Turowski67 | Proposes AGI-compatible methodologies for explainable AI, connecting behavioral and design sciences to develop general-purpose AGI systems for Society 5.0 | Unified methodologies for explainable AI |
Yang et al.68 | Identifies AGI challenges in diagram analysis, focusing on AGI’s ability to understand shape, topology, and content-based image retrieval for technical applications | Advances in diagram-specific retrieval techniques |
Krishnan et al.69 | Investigates AGI integration with disruptive technologies like IoT and autonomous systems, emphasizing its role in adaptive, scalable, and human-centered smart city frameworks | Implementation challenges for nested technologies |
Krishnan et al.70 | Explores AGI principles such as adaptability and multiagent interaction for optimizing disruptive technologies in smart cities, using empirical models to assess AGI’s impact | Framework validation for disruptive AGI technologies |
Long and Cotner71 | Proposes a conceptual framework for AGI development, focusing on generalization, autonomy, and system-level integration for multidomain applications | Scalability of autonomous AGI systems |
Everitt et al.72 | Provides a comprehensive review of AGI safety challenges, addressing goal alignment, system control, and ethical AI deployment strategies | Gaps in comprehensive AGI safety strategies |
