ArticleItalian journal of pediatrics2025
Will artificial intelligence improve residents' quality of life without compromising healthcare quality? A pediatric point-of-view.
Article in Italian journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
conclusionAddressing 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.