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Framework for Government Policy on Agentic and Generative AI in Healthcare: Governance, Regulation, and Risk Management of Open-Source and Proprietary Models

Identification

Clé publique

doi:10.20944/preprints202509.1087.v1

Auteurs

Satyadhar Joshi

Année déclarée

2026

Langue déclarée

en

Type source

article

Première détection

2026-11-09T19:04:47.750146+00:00

Dernière observation des métadonnées

2026-11-09T19:04:47.750146+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Bar-Ilan University · Touro College

Pays d’affiliation déclarés

IL · US

Résumé documentaire — langue originale

The rapid integration of artificial intelligence (AI) in healthcare is defined by a critical dichotomy between open-source and proprietary models, a landscape further complicated by the emergence of autonomous Agentic Generative AI (AGI). This paper provides a comprehensive review and strategic framework to navigate this complex ecosystem. We analyze the technical capabilities, implementation challenges, and governance requirements of both AI paradigms through a systematic examination of current literature and emerging trends. Our findings indicate that while open-source models offer superior transparency, customization, and data privacy-increasingly rivaling proprietary performance in diagnostics-proprietary systems maintain advantages in reliability, support, and integration. Concurrently, AGI introduces transformative potential for healthcare delivery through autonomous decision-making, predictive analytics, and workflow automation. These systems are projected to yield substantial cost savings and reduce diagnostic errors significantly. However, AGI also introduces complex risks ranging from algorithmic bias to regulatory fragmentation. Evidence shows concerning patterns in automated decision appeals and significant financial barriers to implementation that could limit accessibility. To address these challenges, we propose a tiered riskmanagement and governance framework that synthesizes the strengths of both open and closed-source approaches. Our recommendations include the adoption of international certification protocols aligned with global explainability standards, federated learning architectures to ensure privacy while enabling collaboration, and adaptive policymaking to balance innovation with patient safety. The framework provides healthcare organizations with practical strategies for AI adoption while recommending policy directions for responsible deployment. This integrated approach aims to maximize the benefits of both open-source and proprietary AI while mitigating the unique risks posed by agentic systems, ultimately working toward more equitable, efficient, and safe healthcare delivery through artificial intelligence.

Identifiants

OPENALEX

W7126029520

DOI

10.20944/preprints202509.1087.v1 · 10.55524/ijircst.2026.14.1.12

Repères documentaires

Famille documentaire

Doctrine

Types déclarés par les sources

article

Entités naturelles mentionnées

Écosystème · Paysage

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Sources

OpenAlex · Document 1 · Document 2

• DOI · https://doi.org/10.20944/preprints202509.1087.v1
• DOI · https://doi.org/10.55524/ijircst.2026.14.1.12
• OpenAlex · https://openalex.org/W7126029520

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• openalex:w4392871613 · CITES
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• openalex:w4407696986 · CITES
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• openalex:w4413961962 · CITES
• openalex:w4414513303 · CITES
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Statut documentaire

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