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npj Health Systems is a peer-reviewed journal published by Springer Nature, covering Artificial Intelligence in Healthcare and Education, Machine Learning in Healthcare, Electronic Health Records Systems. Over its lifetime it has published 105 papers, which have received 466 citations.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. npj Health Systems 12, 45–58 (2023).
Formats any DOI in npj Health Systems style. No sign-up. |
| Publishes research in | Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare Electronic Health Records Systems Topic Modeling Healthcare Systems and Reforms |
| ISSN | 3005-1959 |
| Citation impact (2-yr) | 4.44 |
| h-index | 11 |
| i10-index | 13 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Cornell University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract Generative artificial intelligence has brought disruptive innovations in health care but faces certain challenges. Retrieval-augmented generation (RAG) enables models to generate more reliable content by leveraging the retrieval of external knowledge. In this perspective, we analyze the possible contributions that RAG could bring to health care in equity, reliability, and personalization. Additionally, we discuss…
Abstract Artificial intelligence (AI) is reshaping healthcare, promising improved diagnostics, personalized treatments, and streamlined operations. Yet a lack of trust remains a persistent barrier to widespread adoption. This Perspective examines the web of trust in AI-assisted healthcare systems, exploring the relationships it shapes, the systemic inequalities it can reinforce, and the technical challenges it poses.…
Large Language Models have expanded the potential for clinical Natural Language Generation (NLG), presenting new opportunities to manage the vast amounts of medical text. However, their use in such high-stakes environments necessitate robust evaluation workflows. In this review, we investigated the current landscape of evaluation metrics for NLG in healthcare and proposed a future direction…
Abstract The growing demand for imaging services in the U.S., driven by an aging population and the rise in chronic diseases, has contributed to a significant radiology workforce shortage. Simultaneously, supply-side constraints, including limited radiology residency positions and substantial retirements, have exacerbated this gap. These imbalances increase patient wait times, risk diagnostic delays, and contribute…
Medical coding is essential for healthcare operations yet remains predominantly manual, error-prone (up to 20%), and costly (up to $18.2 billion annually). Although large language models (LLMs) have shown promise in natural language processing, their application to medical coding has produced limited accuracy. In this study, we evaluated whether fine-tuning LLMs with specialized ICD-10 knowledge…
