The gross tax gap in the U.S is over 600 billion a year. AI and predictive analytics have a transformational opportunity to enhance compliance risk scoring, audit selection, revenue forecasting, and fraud detection at the IRS. This critical literature review summarizes peer-reviewed literature (20202026) on AI adoption to the U.S. tax compliance and revenue systems, evaluating reported outcomes, methodological conflicts, and governance needs. Organized search in SSRN, Google Scholar, Web of Science, Scopus, and government repositories resulted in 37 sources that satisfy pre-determined inclusion criteria and are rated in three levels of evidence. The ML models decrease audit no-change rates by an estimated 15-20 percentage points and forecasting MAPE by 15-30 percent compared to legacy systems, although equity questions are actualized: ROI-optimal classifiers increase audit load on low-income filers unless fixed through regression-based expected-adjustment targets and fairness limits. Governance alignment with NIST AI RMF 1.0, EO 14110, and 26 U.S.C. § 6103 is critical. Equity-by-design, explainable architecture, federated infrastructure, and modernized statutory framework are all necessary to achieve responsible AI adoption.
Issabella Ampofo, David Amoako, Mary Magdalene Linda Yeboah· Magna Scientia Advanced Rese...· 0 citations
The United States healthcare sector grapples with rising cybersecurity threats. Ransomware and data breaches expose millions of protected health information (PHI) records each year, while artificial intelligence (AI) has advanced analytics and supported clinical decisions and tools. AI amplifies both vulnerabilities and protections, but traditional safeguards often struggle or fail to support collaborative model development with stringent HIPAA and HITECH rules. Privacy-preserving machine learning (PPML) techniques offer potential solutions to this tension.
This narrative review draws together peer-reviewed literature from 2020 to 2026 on AI-driven privacy-preserving techniques like federated learning, differential privacy, homomorphic encryption, secure multi-party computation, and blockchain-AI hybrids applied to US healthcare cybersecurity. These tools allow decentralized training, encrypted operations, and auditable partnerships that curb re-identification, inference attacks, and centralized data risks.
Key findings highlight federated learning’s maturity in multi-institutional applications, differential privacy’s solid defenses for group-level analysis, and the promise of hybrids to overcome individual limitations such as computational overhead and expansion barriers. However, persistent challenges include resource demands, potential bias amplification, adversarial vulnerabilities, and limited real-world longitudinal evidence.
The review calls for uniform testing standards, quantum-proof designs, and policy boots to speed uptake. Such methods strengthen privacy alongside function, paving the way for reliable AI use that protects patients, cuts breach damage, and promotes digital health innovation in an increasingly threatened ecosystem.
Isaiah Thompson Ocansey, Mary Magdalene Linda Yeboah· Magna Scientia Advanced Rese...· 0 citations
The present study is a synthesis of multidisciplinary literature that would create a combined conceptual framework of how data-driven monitoring systems would improve the internal financial controls within US government agencies.
H. A. K. Dankwah, Mary Magdalene Linda Yeboah· Journal of Economic, Finance...· 0 citations
This review synthesizes cross-sector evidence to identify structural, operational, and technological barriers to harmonization, while proposing an integrative governance perspective grounded in recent empirical and policy literature.
William Asare Yirenkyi, G. Apaflo, Matilda Konotey et al.· Magna Scientia Advanced Rese...· 0 citations
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