Go Back Research Article May, 2025

Fair and Accountable AI in Healthcare: Building Trustworthy Models for Decision-Making and Regulatory Compliance

Author Researcher v Author Vijaybhasker Pagidoju

Abstract

While this research aims for mitigating bias, regulation, accountability and transparency in fair and accountable AI in healthcare, these can be extended to other health contexts. It presents the evaluation of frameworks, tools and practices towards increasing trustworthiness of AI based clinical decision making. Leads to the finding that responsible development, infrastructure resilience and continuous auditing are required to adopt ethical and compliant AI development.

Keywords

fairness in ai for healthcare bias mitigation in medical ai explainable ai regulatory compliance hipaa cms fda ethical ai ai transparency accountable machine learning healthcare infrastructure health insurance ai auditing medicare eligibility models ai governance data privacy in healthcare ai-driven decision systems site reliability engineering in healthcare observability in medical ai systems machine learning operations (mlops) automated compliance monitoring ai model validation infrastructure resilience responsible ai trustworthy healthcare ai
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Volume 6
Issue 3
Pages 26-38
ISSN 3067-7394