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 mitigati

SUBMITTER: Li Y 

PROVIDER: S-EPMC12615213 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature