Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions

Abstract

Purpose: This study aims to systematically identify and address key barriers to misdiagnosis in AI-driven medical diagnostics. The main research question is how technical limitations, ethical concerns, and unclear accountability hinder safe and equitable use of AI in real-world clinical practice, and what integrated solutions can minimize errors and promote trust. Methods: We conducted a literature review and case analysis across major medical fields, evaluating failure modes such as data pathology, algorithmic bias, and human-AI interaction. Based on these findings, we propose a multidimensional framework combining technical strategies-such as dynamic data auditing and explainability engines-with ethical and policy interventions, including federated learning for bias mitigation and blockchain-based accountability. Results: Our analysis shows that misdiagnosis often results from data bias, lack of model transparency, and ambiguous responsibility. When applied to published case examples and comparative evaluations from the literature, elements of our framework are associated with improvements in diagnostic accuracy, transparency, and equity. Key recommendations include bias monitoring, real-time interpretability dashboards, and legal frameworks for shared accountability. Conclusion: A coordinated, multidimensional approach is essential to reduce the risk of misdiagnosis in AI-supported diagnostics. By integrating robust technical controls, clear ethical guidelines, and defined accountability, our framework provides a practical roadmap for responsible, transparent, and equitable AI adoption in healthcare-improving patient safety, clinician trust, and health equity.

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