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PREreview de Perceptions, Preparedness, and Challenges of Artificial Intelligence Integration in Government Healthcare Institutions in Al Buraimi Governorate, Oman: A Cross‑Sectional Study

par Julian Rodriguez, Jr.
Publié
DOI
10.5281/zenodo.22986284
Licence
CC0 1.0

PREreview: LLMs4All: A Review of Large Language Models Across Academic Disciplines

  • Reviewer: Julian Rodriguez, Jr., FRSA, MRES, M.ISRM

  • Preprint DOI: 10.20944/preprints202510.1383.v1 / arXiv:2509.19580

  • Authors: Yanfang Ye et al. (University of Notre Dame)

Review Summary

LLMs4All provides an ambitious, cross-disciplinary synthesis evaluating the deployment and performance of Large Language Models across three major academic pillars: (1) Arts, Letters, and Law; (2) Economics and Business; and (3) Science and Engineering. Moving beyond standard benchmark evaluations, Ye et al. examine how domain-specific contexts—ranging from legal document analysis and financial auditing to macro modeling and life sciences—reframe LLM capabilities and failure modes. The paper stands as an essential reference mapping generative AI's impact across scholarly workflows.

Key Strengths

  • Unrivaled Taxonomical Scope: The authors effectively break down disciplinary silos, offering concrete sub-field mappings (e.g., behavioral economics, game-theoretic interactions, tax compliance, and legal judgment prediction) rather than generic AI capabilities.

  • Balanced Failure-Mode Analysis: The survey does not shy away from structural constraints, explicitly addressing context window limitations, reasoning degradation, and domain-specific hallucination risks.

  • Actionable Roadmap: By categorizing disciplinary benchmarks alongside current deployment methods, the survey serves as a practical diagnostic manual for institutional researchers.

Constructive Considerations & Theoretical Enhancements

To further strengthen the analytical framing in Sections 3 and 4 (Law, Policy, and Economics), future iterations of this work could incorporate several key institutional governance dimensions:

  1. Accounting for Systemic Latency & Administrative Shadows: While the survey notes LLM limitations in complex legal and economic reasoning, it primarily views these as algorithmic accuracy deficits. When LLMs are embedded into regulatory compliance or public administration, accuracy errors manifest as Systemic Intent Shadows (Rodriguez, 2026)—creating false-positive compliance noise and administrative latency that distorts organizational outcomes.

  2. The Risk of Behavioral Collapse in Regulated Workflows: In legal drafting and auditing (Sections 3.5 & 4.3), deployers often implement hyper-strict automated validation checks. Over-relying on LLM oversight without clear procedural guardrails triggers the Zero-Defect Paradox (Rodriguez, 2025/2026), where fear of algorithmic non-compliance causes operational paralysis among frontline staff.

  3. Asymmetric Infrastructure Obsolescence: In cross-border business and trade economics (Section 4.1), the rapid rollout of proprietary LLM infrastructure risks creating Asymmetric Obsolescence (Rodriguez, 2026), where institutions in developing or peripheral jurisdictions are forced to rely on foreign decision engines that misinterpret local market context as noise.

Concluding Remarks & Recommendation

Overall, LLMs4All is a timely, rigorous, and masterfully organized survey that bridges technical AI advancements with real-world domain application. Ye et al. have delivered an indispensable foundation for researchers, institutional policy makers, and cross-disciplinary scholars alike. I highly recommend this preprint for formal publication and look forward to seeing its continued impact across the open-science ecosystem.

Recommended References for Future Iterations

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.