Problem-Method-Value Mapping in AI-Driven Cross-Domain Innovation: A Literature Synthesis (2022-2026)

Yuan Ran

special issue

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

Problem-Method-Value Mapping in AI-Driven Cross-Domain Innovation: A Literature Synthesis (2022-2026)

Yuan Ran

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

Abstract

Artificial intelligence (AI) has become a universal problem-solving tool across diverse sectors, but its cross-domain impact hinges on the precise alignment between domain-specific problems, tailored AI methods, and tangible value outcomes. This review synthesizes 9 key studies (2022-2026) to establish a "problem-method-value" (PMV) mapping framework, analyzing how AI addresses core challenges in healthcare, quantum science, digital commerce, cybersecurity, and finance. The framework identifies five domain-specific problem types—precision demand, system complexity, privacy-collaboration conflict, risk management dilemma, and resource constraint—and maps them to corresponding AI methods (hybrid architectures, unsupervised learning, privacy-enhancing technologies, multi-agent reinforcement learning, LSTM models) and value outcomes (diagnostic accuracy, modeling efficiency, compliant collaboration, risk-cost balance, resource optimization). Findings reveal that successful cross-domain AI innovation is defined by the tight coupling of these three components, providing a practical tool for researchers and practitioners to design purpose-driven AI solutions.

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

Problem-Method-Value
AI-Driven Cross-Domain Innovation

How to Cite

Ran, Y. (2026). Problem-Method-Value Mapping in AI-Driven Cross-Domain Innovation: A Literature Synthesis (2022-2026). International Journal of Multidisciplinary Research, 2(3). Retrieved from https://ijmrscience.org/index.php/ijmr/article/view/156

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).