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The incorporation of Artificial Intelligence (AI) technology into several industries has significantly impacted the usual workflows and processes in recent years, including the healthcare industry. Human Resource Management (HRM) is essential in healthcare businesses as it is responsible for the recruitment, training, and the retention of skilled staff members who are capable of providing high-quality patient care. This paper investigates different methods in which AI is used in HRM in the healthcare industry on the basis of existing research in the area. It analyzes how AI affects recruitment, talent management, workforce optimization, and employee well-being. This paper also discusses the challenges and future prospects of AI-driven approaches in HRM practices. It explores how these approaches are changing the way healthcare organizations operate and improving patient outcomes. The results provide some valuable contributions to the field of artificial intelligence in the healthcare sector. Initially, the chapter gives a factual foundation for the current presumptions on the implementation and difficulties of artificial intelligence in the healthcare domain. Further, it shows how artificial intelligence provides numerous opportunities to expedite Human Resource operations by offering automated applicant screening, customized learning systems, optimizing the workforce and enhancing employee engagement. Although AI has the capacity to revolutionize HRM practices in the healthcare industry, it also presents some challenges and obstacles. In order to ensure that AI-driven solutions promote fairness, transparency, and equity, it is crucial to address issues such as algorithmic bias, privacy of data and the impact on the human workforce in a deliberate manner. In addition, healthcare firms need to invest funds for implementing rigorous cyber security measures in order to ensure the privacy of patient and employee data from cyber-attacks and potential breaches.
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Stored only when both papers are in the corpus — 13 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.