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AI in Human Resource Risk Management: Research in Tehran Medical Universities
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Mohammad Javadizadeh1   , Mohammad Fatehi Dabanlou * , Zahra Houshmand Neghabi1 , Akbar Bagheri1 |
1- Department of Management and Accounting, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran.
Department of Management and Accounting, Islamshahr Branch, Islamic Azad University, Tehran, Iran., Department of Management and Accounting, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran. , m.fatehidabanlou@iau.ir |
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Abstract: (728 Views) |
Introduction: The advent and advancement of generative artificial intelligence have fundamentally transformed research paradigms, particularly in data-driven human resource management analytics. This study aims to present an innovative AI-driven framework for predicting human resource risks within medical universities, identifying the most significant predictive factors for these risks.
Methods: Employing a mixed-methods (qualitative-quantitative) approach, this research was conducted in 2025 with the participation of 24 experts from the fields of human resources, information technology, financial management, industrial engineering, and AI instructors at Tehran’s medical universities. In the qualitative phase, semi-structured interviews with experts were conducted, and the resulting data were analyzed using the specialized software MAXQDA and the thematic analysis method to identify key human resource risks. In the quantitative phase, real-world data from the human resource information systems of selected Tehran medical universities (covering the period 2022-2024) comprising 12 key variables (e.g., absenteeism, performance evaluations, contract type, service years, etc.) were cleaned, normalized, and coded. Subsequently, these data were analyzed using the Python programming language and its machine learning libraries, including Scikit-learn for Random Forest and Logistic Regression models, and TensorFlow/PyTorch for the MLP neural network.
Results: The results indicate that the use of artificial intelligence algorithms—particularly neural networks and machine learning—plays an effective role in predicting and modeling human-resource risks. By leveraging data mining and big-data analytics, these approaches can identify latent patterns associated with employee performance, turnover intentions, and behavioral indicators, thereby improving managerial decision-making through intelligent risk prediction and early-warning systems. The evaluation of predictive models further suggests that their performance is strongly dependent on data quality and algorithm design. Despite the demonstrated benefits, several challenges remain, including data limitations, the need for appropriate technical infrastructure, and ethical considerations in implementing these technologies.
Conclusions: This study, employing a mixed-methods (qualitative-quantitative) approach, documented the efficacy of Artificial Intelligence (AI) in predicting and managing Human Resources risks within medical universities. The Artificial Neural Network (ANN) model, achieving an accuracy of 0.92, substantiated its superiority in modeling complex risk patterns compared to classical algorithms. The research’s novelty lies in the integration of qualitative findings concerning data and managerial challenges with advanced quantitative modeling, alongside the provision of an operational management dashboard featuring automated alerting capabilities. This dashboard aims to facilitate decision-making and mitigate costs associated with HR risks.
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| Keywords: Risk Management, Human Resource, Artificial Intelligence, Medical University |
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Full-Text [PDF 372 kb]
(722 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2026/Apr/Fri | Accepted: 2026/May/Sat | Published: 2025/Jan/Wed
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