Comprehensive Study on the Use of Artificial Intelligence to Minimize Bias in Healthcare Succession Management
Abstract
In healthcare, succession planning mitigates the risk of leadership vacancies during retirements, turnover, and staffing shortages. However, current practices to choose a next leader often rely on subjective judgment, which can reinforce bias and limit fairness in leadership decisions. This study examines whether Artificial Intelligence (AI) can improve succession planning by supporting more objective and data-informed assessment of talent. The findings suggest that AI may help organizations define clearer criteria, identify high-potential candidates, and expand opportunities for underrepresented groups. At the same time, the study shows that AI must be used carefully, with strong data governance and collaboration among human resources professionals, data scientists, and clinical leaders. Without these safeguards, AI may reproduce the same biases it is meant to reduce. Used responsibly, AI has the potential to make healthcare leadership selection more transparent, equitable, and inclusive.
Keywords
References
Most read articles by the same author(s)
- Dr. Aarav Sharma, Dr. Meera Kulkarni, An Integrated NDVI-Driven Predictive Model for Assessing Protein Concentration in Rice Crops and Nitrogen Status in Rice Leaves Through Aerial Imaging and Regression Analysis , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Dr. Ali Hosseini, Deep Convolutional Neural Network-Based Adaptive Chatbot Framework for Personalized Educational Support in Autism Spectrum Disorder , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Rizky Pratama, Dinda Maharani, Computational Representation and Structural Enhancement of Nature-Derived Collective Monitoring Behaviors , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Elara V. Sorenson, Deep Contextual Understanding: A Parameter-Efficient Large Language Model Approach To Fine-Grained Affective Computing , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Michael Andersson, Optimizing Continuous Schema Evolution and Zero-Downtime Microservices in Enterprise Data Architectures , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. James William Carter, Dr. Emily Rose Thompson, Class-Imbalance Aware Deep Learning Framework for Accurate Rice Seed Germination Classification and Robust Seedling Identification , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Dr. Jakob Schneider, ALGORITHMIC INEQUITY IN JUSTICE: UNPACKING THE SOCIETAL IMPACT OF AI IN JUDICIAL DECISION-MAKING , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Serhii Yakhin, Comparative Review of Clean Architecture and Vertical Slice Architecture Approaches for Enterprise .NET Applications , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Dr. Alessia Romano, Prof. Marco Bianchi, DEVELOPING AI ASSISTANCE FOR INCLUSIVE COMMUNICATION IN ITALIAN FORMAL WRITING , International Journal of Advanced Artificial Intelligence Research: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Ayesha Siddiqui, ENHANCED IDENTIFICATION OF EQUATORIAL PLASMA BUBBLES IN AIRGLOW IMAGERY VIA 2D PRINCIPAL COMPONENT ANALYSIS AND INTERPRETABLE AI , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 02 (2025): Volume 02 Issue 02
Similar Articles
- Dr. Jae-Won Kim, Dr. Sung-Ho Lee, NAVIGATING ALGORITHMIC EQUITY: UNCOVERING DIVERSITY AND INCLUSION INCIDENTS IN ARTIFICIAL INTELLIGENCE , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 07 (2025): Volume 02 Issue 07
- Adrian T. Blackmoor, Digital Lending Transformation Through Real Time Artificial Intelligence Based Credit Analytics , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Olabayoji Oluwatofunmi Oladepo., Explainable Artificial Intelligence in Socio-Technical Contexts: Addressing Bias, Trust, and Interpretability for Responsible Deployment , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 09 (2025): Volume 02 Issue 09
- Dr. Jakob Schneider, ALGORITHMIC INEQUITY IN JUSTICE: UNPACKING THE SOCIETAL IMPACT OF AI IN JUDICIAL DECISION-MAKING , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Dr. Wei Zhang, Dr. Li Chen, An Intelligent Knowledge-Driven Clinical Decision Support Framework for Predictive Comorbidity Risk Assessment and Healthcare Decision-Making , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Eleni Markou, Narrative Intelligence In The Age Of Generative Ai: Integrating Computational Storytelling, Transformer Architectures, Ethical Governance, And Consumer Impact , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Elena M. Ruiz, Integrating Big Data Architectures and AI-Powered Analytics into Mergers & Acquisitions Due Diligence: A Theoretical Framework for Value Measurement, Risk Detection, and Strategic Decision-Making , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 09 (2025): Volume 02 Issue 09
- Dr. Haruto Nakamura, Dr. Yui Takahashi, A Deep Unsupervised Artificial Intelligence Model for Automated Prostate Cancer Prediction Through Latent Pattern Discovery and Clinical Data Analysis , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Amir Hosseini, A Intelligent Edge-Cloud Integration for Resilient and Real-Time AI Decision Systems , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Takumi Suzuki, Mio Tanaka, Scalability Constraints in AI-Driven Construction Management: Opportunities for Robotics and LLM Integration , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
You may also start an advanced similarity search for this article.