From Models to Hybrid Intelligence: Emerging AI Approaches for Engineering Applications

Q: What does this research mean for the field?

Emerging artificial intelligence approaches in engineering are transitioning from standalone models to hybrid intelligence systems to support complex tasks such as optimization, anomaly detection, and decision support. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Zbigniew Gomółka; Damian Kordos; Ewa Dudek–Dyduch

doi:10.3390/app16178864

September 6, 2026Applied SciencesOpen Access

From Models to Hybrid Intelligence: Emerging AI Approaches for Engineering Applications

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Key Points

To evaluate the transition of artificial intelligence applications in engineering from standalone models to integrated hybrid intelligence frameworks.
Reviewed contemporary artificial intelligence paradigms across varied engineering domains.
Categorized algorithmic roles across optimization, predictive modeling, anomaly detection, computer vision, and clinical decision support.
Identified a methodological shift from isolated predictive algorithms toward hybrid intelligence structures that blend computational power with human expertise.
Demonstrated that multi-domain engineering tasks increasingly depend on unified systems combining real-time computer vision, anomaly detection, and automated decision support.

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Implication

Technical review demonstrates the integration of artificial intelligence into engineering systems, highlighting the transition toward collaborative hybrid intelligence.

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Gomółka et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd80458e84d0ff5b471e4 https://doi.org/10.3390/app16178864

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From Models to Hybrid Intelligence: Emerging AI Approaches for Engineering Applications

Key Points

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Implication

Discussion

Cite This Study

From Models to Hybrid Intelligence: Emerging AI Approaches for Engineering Applications

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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