Possibilities to reduce the human factor in diagnostic systems

Annotatsiya

Human factors—fatigue, cognitive bias, workload, and variability in expertise—remain significant contributors to diagnostic errors across healthcare domains. Automated and assistive diagnostic systems can reduce these risks by augmenting clinical decision-making, standardizing interpretation, and accelerating workflows. This article reviews algorithmic strategies (rule-based automation, assistive and fully automated ML systems), human-in-the-loop designs, and interface considerations that mitigate human error. We analyze system-level trade-offs: accuracy gains, reduced time-to-diagnosis, changes in clinician workload, and the potential for automation bias. A synthetic experimental study compares methods across general practice, emergency care, radiology, and primary care, providing tables and charts that illustrate improved accuracy and reduced human error rates with assistive and fully automated approaches. We conclude with deployment recommendations—phased rollouts, continual validation, explainability tools, and governance—to ensure that automation augments rather than undermines clinical safety and trust.