Inter-Domain Adaptation Mechanisms of AI Innovation: How AI Breaks Through Sectoral Barriers (2022-2026)

Li Peng

special issue

Vol. 2 No. 3 (2026): International Journal of Multidisciplinary Research

Inter-Domain Adaptation Mechanisms of AI Innovation: How AI Breaks Through Sectoral Barriers (2022-2026)

Li Peng

PDF

Submitted: March 25, 2026
Published: 2026-04-02

Abstract

The cross-domain expansion of artificial intelligence (AI) is not a simple technical transplantation but a systematic adaptation process that aligns AI capabilities with domain-specific rules, needs, and contexts. This review synthesizes 9 key studies (2022-2026) to identify three core inter-domain adaptation mechanisms: technical adaptation (reconfiguring AI architectures to fit domain-specific data and task characteristics), scenario adaptation (tailoring AI functions to match sectoral application scenarios and user demands), and governance adaptation (aligning AI systems with domain regulatory frameworks and ethical norms). By analyzing how these mechanisms enable AI to overcome "domain incompatibility" in healthcare, quantum science, digital commerce, cybersecurity, and finance, the paper reveals that successful cross-domain AI relies on the dynamic balance between technical generality and domain specificity. The findings provide a new analytical framework for understanding AI’s cross-sectoral diffusion, offering guidance for researchers designing adaptable AI systems and practitioners implementing AI across diverse fields.

References

Chang, C., Fu, M., Chen, X., et al. (2025, November). Research on PDU-Net Lung Nodule Segmentation Algorithm Based on Path Aggregation and Dual Attention. In 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1897-1900). IEEE.
Wu, A. K., Primeau, L., Zhang, J., et al. (2025). Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning. arXiv preprint arXiv:2510.03018.
Yi, X. (2026). A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce.
Zhou, D. (2025, December). M-VP2: Microservice-Oriented Vulnerability Patch Planning-A Cost-Aware Approach using Multi-Agent Reinforcement Learning. In 2025 5th International Conference on Computer, Internet of Things and Control Engineering (CITCE) (pp. 248-254). IEEE.
Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.
Liu, T. (2022, December). Financial Constraint’Impact on Firms’ ESG Rating Based on Chinese Stock Market. In 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022) (pp. 1085-1095). Atlantis Press.
Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.
Yi, X. (2026). Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization. Frontiers in Business and Finance, 3(1), 102-113.
Li, H., & Liu, T. (2023). Portfolio optimization based on the LSTM forecasting model. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (Vol. 48, No. 1, pp. 97-106).

Keywords

artificial intelligence
Inter-Domain Adaptation Mechanisms
Sectoral Barriers

How to Cite

Peng, L. (2026). Inter-Domain Adaptation Mechanisms of AI Innovation: How AI Breaks Through Sectoral Barriers (2022-2026). International Journal of Multidisciplinary Research, 2(3). Retrieved from https://ijmrscience.org/index.php/ijmr/article/view/155

This work is licensed under a Creative Commons Attribution 4.0 International License.

[1] Chang, C., Fu, M., Chen, X., et al. (2025, November). Research on PDU-Net Lung Nodule Segmentation Algorithm Based on Path Aggregation and Dual Attention. In 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1897-1900). IEEE.

[2] Wu, A. K., Primeau, L., Zhang, J., et al. (2025). Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning. arXiv preprint arXiv:2510.03018.

[3] Yi, X. (2026). A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce.

[4] Zhou, D. (2025, December). M-VP2: Microservice-Oriented Vulnerability Patch Planning-A Cost-Aware Approach using Multi-Agent Reinforcement Learning. In 2025 5th International Conference on Computer, Internet of Things and Control Engineering (CITCE) (pp. 248-254). IEEE.

[5] Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.

[6] Liu, T. (2022, December). Financial Constraint’Impact on Firms’ ESG Rating Based on Chinese Stock Market. In 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022) (pp. 1085-1095). Atlantis Press.

[7] Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.

[8] Yi, X. (2026). Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization. Frontiers in Business and Finance, 3(1), 102-113.

[9] Li, H., & Liu, T. (2023). Portfolio optimization based on the LSTM forecasting model. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (Vol. 48, No. 1, pp. 97-106).