Fair and Accountable AI in Healthcare: Building Trustworthy Models for Decision-Making and Regulatory Compliance
DOI:
https://doi.org/10.63397/ISCSITR-IJCSE_2025_06_03_003Keywords:
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 AIAbstract
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.
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Copyright (c) 2025 Vijaybhasker Pagidoju (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
