Strategic Management of Algorithmic Bias: A Review of AI-Driven Clinical Decision Support in Healthcare Organizations

DOI

10.2991/978-94-6239-618-0_4How to use a DOI?

Keywords

CDSS; Artificial Intelligence (AI); Retrieval Strategy

Abstract

Clinical Decision Support Systems (CDSS), which use Artificial Intelligence (AI), have revolutionized healthcare delivery on an unparalleled scale, pace, and furor with better diagnostics, personalized treatment suggestions, and facilitated clinical procedures. Nevertheless, the recent discoveries also brought the issues of the prevalence of the algorithmic bias in these systems that may fuel the health inequities and threaten the safety of the patients. The present paper reviews the existing practices and policies of strategic approaches to algorithmic bias management in AI-based CDSS within healthcare organizations by highlighting the identification, reduction, and control of bias in the clinical decision-making process.

The paper has threefold objectives, namely: 1) synthesizing the literature on the causes and Implications of algorithmic bias in CDSS, 2) assessing the available frameworks, methods, and approaches to reduce bias, and 3) making actionable insights and suggestions to healthcare policymakers, administrators, and AI developers. The systematic literature review methodology was employed, including high-impact databases of factors. The search terms were used to select the articles, and preference featured in peer-reviewed research, case studies, and industry reports that emphasized clinical AI systems, fairness, interpretability, and governance.

Fairness-aware machine learning, human-in-the-loop interventions, and continuous monitoring, which comprise various bias mitigation strategies, were analyzed and revealed the role of organizational governance and responsible implementation of AI in ensuring fairness. The review has added value to the strategic interpretation of bias control in clinical AI, and its recommendations can be used to maximize the fairness, transparency, and credibility of AI-assisted healthcare decision-making.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Girish Chandra Bhatt
AU  - Manoj Kumar Gopaliya
PY  - 2026
DA  - 2026/03/16
TI  - Strategic Management of Algorithmic Bias: A Review of AI-Driven Clinical Decision Support in Healthcare Organizations
BT  - Proceedings of 3rd International Conference on Library & Technology on “Artificial Intelligence and Humanities in Library and Education 4.0 (AIHLE 2025)
PB  - Atlantis Press
SP  - 34
EP  - 49
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-618-0_4
DO  - 10.2991/978-94-6239-618-0_4
ID  - Bhatt2026
ER  -