An Agentic Framework for Compliant, Ethical and Trustworthy GenAI Applications in Healthcare

Abstract

Recent progress in generative artificial intelligence (GenAI) has yielded significant advancements in healthcare, affecting radiology, medical imaging, drug development, patient diagnostics, and supply chain optimisation.These innovations promise more improved diagnoses and time-saving cost-effectiveness.However, GenAI's rapid implementation poses significant challenges for meeting regulatory, ethical, and trustworthiness standards.These challenges include data privacy issues, reproducibility concerns, algorithmic bias in training data causing disparities in outcomes, and a lack of transparency and explainability.Unresolved, these issues could negatively affect the public's confidence in and perception of GenAI systems.Addressing these challenges, international AI governance frameworks, including the EU AI Act and WHO guidelines, prioritize regulatory adherence, trustworthiness, and the explainability of healthcare AI systems.While such frameworks have expanded, a deficiency remains in translating policy into effective compliance mechanisms.We propose a Compliance Agentic Model (CAM) framework to help organizations comply with GenAI and machine learning (ML)-based solutions.The CAM framework establishes trustworthiness in GenAI applications used in healthcare, ensuring alignment with organizational values and ethical standards to enhance accountability and regulatory adherence.

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