National Framework for Agentic Generative AI in Cancer Care: Policy Recommendations and System Architecture

Abstract

Agentic AI is emerging, characterized by autonomous, goal-driven systems capable of reasoning, planning, and executing complex workflows. This paper provides a comprehensive review of the foundational principles of Agentic AI, its current applications in cancer diagnostics, treatment planning, and drug discovery, and the technical and ethical challenges that must be addressed. We analyze real-world implementations, such as multi-agent orchestration platforms for tumor boards and project future trajectories. Agentic Gen AI's impact is most profound in oncology, where the complexity of data and the critical need for personalized treatment present a significant challenge. This paper provides a comprehensive analysis of Agentic AI, beginning with its technical foundations and architectural frameworks. We detail its transformative applications across the cancer care continuum-from enhanced diagnostics and personalized treatment planning to drug discovery and operational efficiency. The paper further analyzes the critical technical, ethical, and regulatory challenges to integration. Beyond a review, we propose a national strategy policy recommendations for regulatory adaptation, a detailed economic model analyzing implementation costs and return on investment, and a technical roadmap for future applications such as generative digital twins and autonomous scientific discovery. We argue that Agentic AI represents a fundamental transformation in healthcare delivery, poised to enhance the precision, efficiency, and accessibility of cancer care, contingent on the successful navigation of these multifaceted challenges and the adoption of the responsible framework we outline.

Keywords: Agentic AI, Artificial Intelligence, Healthcare, Oncology, Cancer Diagnostics, Multi-Agent Systems, Personalized Medicine, AI Ethics