Will artificial intelligence improve residents' quality of life without compromising healthcare quality? A pediatric point-of-view.

Abstract

Background: The integration of artificial intelligence (AI) and advanced large language models in medical education and clinical practice is reshaping healthcare. These technologies have significant potential to enhance training experience and quality of life for medical residents. By automating routine tasks such as documentation and preliminary data analysis, AI-driven models can significantly reduce the workload, enabling residents to focus more on direct patient care and hands-on learning opportunities.

Main body: AI-driven support in diagnostics and decision-making may also reduce diagnostic errors, fostering a safer and more efficient healthcare environment. Furthermore, by alleviating administrative burdens, AI could play a critical role in mitigating resident burnout, contributing to a more resilient healthcare workforce and ultimately improving the continuity and quality of patient care. However, the adoption of AI in medical practice poses challenges. Automation risks reducing essential clinical skills, and over-reliance on AI may impact on professional autonomy and the development of diagnostic capacities. Concerns also persist regarding biased data, data security, legal issues, and the transparency in AI-driven decision-making processes.

Conclusion: Addressing these challenges requires collaboration among healthcare professionals, AI developers and policymakers, as well as ethical frameworks and country-specific regulations. Only through a balanced and collaborative approach can we unlock AI's full potential to create a more efficient, equitable, and patient-centered healthcare system.