How artificial intelligence reduces human bias in diagnostics?

Artur Luczak; Artur Luczak

doi:10.3934/bioeng.2025004

AIMS Bioengineering

2025, Volume 12, Issue 1: 69-89. doi: 10.3934/bioeng.2025004

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Research article Special Issues

How artificial intelligence reduces human bias in diagnostics?

Artur Luczak ^,

Canadian Centre for Behavioural Neuroscience, University of Lethbridge, AB, Canada

Received: 10 October 2024 Revised: 07 December 2025 Accepted: 07 February 2025 Published: 12 February 2025

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.
- movement disorders diagnosis,
- bias reduction in healthcare,
- automated behavioral scoring,
- data-driven diagnostics,
- interpretable machine learning
Citation: Artur Luczak. How artificial intelligence reduces human bias in diagnostics?[J]. AIMS Bioengineering, 2025, 12(1): 69-89. doi: 10.3934/bioeng.2025004

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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.

Acknowledgments

The author developed AI agents to work with them, like with a good MSc student. Agents helped to identify the most relevant literature, design a plan for the paper, implement suggested improvements, and draft paper sections and rewrite them based on comments provided by the author. The author assumes full responsibility for the accuracy of the content presented here.

Conflict of interest

The author has no conflicts of interest to declare.

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