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special issue

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

Cross-Domain Adoption of AI: Core Challenges and Practical Solutions—A Literature Review (2022-2026)

  • Mu Te
Submitted
March 25, 2026
Published
2026-04-02

Abstract

While artificial intelligence (AI) has demonstrated transformative potential across healthcare, quantum science, digital commerce, cybersecurity, and finance, its large-scale cross-domain adoption is hindered by multifaceted challenges. This review synthesizes 10 recent studies (2022-2026) to identify three overarching barriers: technical incompatibility with domain-specific demands, privacy and regulatory compliance risks, and resource constraints for small-to-medium enterprises (SMEs) and specialized fields. Through analyzing innovative solutions proposed in the literature—including hybrid AI architectures, privacy-enhancing technologies (PETs), and adaptive algorithmic frameworks—this paper highlights how tailored AI implementations address these challenges. The findings underscore the importance of domain-driven AI design and cross-disciplinary collaboration, providing actionable insights for researchers, practitioners, and policymakers aiming to accelerate responsible AI adoption across sectors.

References

  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. Yue, Y., Khanal, A., Lyu, T., Weissman, S., & Liang, C. (2025, May). EHR Phenotyping Methods for Measuring Treatment Adherence Among People Living With HIV in All of Us: Towards Disparities and Inequalities in HIV Care Continuum. In AMIA Annual Symposium Proceedings (Vol. 2024, p. 1294).
  10. 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).