Beyond bias: using AI to reduce diagnostic noise and manage novelty in clinical reasoning

This study examines AI's capacity to mitigate noise-related diagnostic errors, evaluates its impact on accuracy, and explores the interplay between AI-driven efficiency and human clinical reasoning, particularly in rare or complex cases. Background: Diagnostic errors in clinical reasoning are significantly influenced by noise - random unwanted variability in expert judgments - distinct from cognitive biases. Despite debiasing efforts, noise persists, contributing to adverse events. Artificial intelligence (AI) offers potential solutions but faces limitations in addressing novel diagnostic scenarios requiring creative reasoning.

A narrative review and conceptual analysis synthesises the literature on noise in medical decision-making, AI applications in healthcare, and clinical reasoning frameworks. Case studies (e.g., radiology, pathology) and empirical data on AI performance are reviewed, alongside discussions of noise types (occasion, pattern, group-level) and the role of AI in decision hygiene.

AI reduces noise by minimising unwanted variability in pattern recognition tasks (e.g., imaging analysis), improving diagnostic consistency. However, AI struggles with novel hypotheses, creative reasoning, and contextual interpretation, remaining reliant on human oversight. AI's inability to replicate human creativity limits its utility in rare disease diagnosis. Hybrid human-AI systems show promise but require balancing AI noise reduction capacities with human contextual judgment.

AI mitigates noise-driven errors in structured tasks but cannot replace human reasoning in complex, uncertain scenarios. Optimal diagnostic accuracy demands the integration of AI's analytical strengths with clinicians' creative and contextual reasoning. Future research should prioritise AI collaboration frameworks and address AI limitations in novelty-driven disease diagnostics.