How artificial intelligence reduces human bias in diagnostics?

Abstract

Accurate diagnostics of neurological disorders often rely on behavioral assessments, yet traditional methods rooted in manual observations and scoring are labor-intensive, subjective, and prone to human bias. Artificial Intelligence (AI), particularly Deep Neural Networks (DNNs), offers transformative potential to overcome these limitations by automating behavioral analyses and reducing biases in diagnostic practices. DNNs excel in processing complex, high-dimensional data, allowing for the detection of subtle behavioral patterns critical for diagnosing neurological disorders such as Parkinson's disease, strokes, or spinal cord injuries. This review explores how AI-driven approaches can mitigate observer biases, thereby emphasizing the use of explainable DNNs to enhance objectivity in diagnostics. Explainable AI techniques enable the identification of which features in data are used by DNNs to make decisions. In a data-driven manner, this allows one to uncover novel insights that may elude human experts. For instance, explainable DNN techniques have revealed previously unnoticed diagnostic markers, such as posture changes, which can enhance the sensitivity of behavioral diagnostic assessments. Furthermore, by providing interpretable outputs, explainable DNNs build trust in AI-driven systems and support the development of unbiased, evidence-based diagnostic tools. In addition, this review discusses challenges such as data quality, model interpretability, and ethical considerations. By illustrating the role of AI in reshaping diagnostic methods, this paper highlights its potential to revolutionize clinical practices, thus paving the way for more objective and reliable assessments of neurological disorders.

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