UDTR Global Rights of Nature Radar

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

Identification

Public canonical key

doi:10.20944/preprints202509.1087.v1

Authors

Satyadhar Joshi

Reported year

2026

Reported language

en

Source type

article

First detected

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

Last metadata observation

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

Last HTTP verification

Not checked

Institutions reported by the source

Bar-Ilan University · Touro College

Reported affiliation countries

IL · US

Documentary abstract — original language

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.

Identifiers

OPENALEX

W7126029520

DOI

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

Documentary context

Documentary family

Scholarship

Source-reported types

article

Natural entities mentioned

Ecosystem · Landscape

Entity mentions are not evidence of legal recognition. Jurisdiction studied is separate from author affiliation country.

Sources

OpenAlex · Record 1 · Record 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

Documented bibliographic relations

Source-declared references and links; they do not establish legal support or UGLC qualification. Up to 20 outgoing references are displayed.

• openalex:w4392871613 · CITES
• openalex:w4404787535 · CITES
• openalex:w4407696986 · CITES
• openalex:w4408468381 · CITES
• openalex:w4413961962 · CITES
• openalex:w4414513303 · CITES
• openalex:w7108475821 · CITES
• openalex:w7109166677 · CITES

0 incoming references within the detected corpus

Documentary status

Relevance pending

Library

This identity is not yet included in the Legal Library.