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npj Artificial Intelligence is a peer-reviewed journal published by Springer Nature, covering Artificial Intelligence in Healthcare and Education, Topic Modeling, Machine Learning in Materials Science. Over its lifetime it has published 125 papers, which have received 406 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 Artificial Intelligence 12, 45–58 (2023).
Formats any DOI in npj Artificial Intelligence style. No sign-up. |
| Publishes research in | Artificial Intelligence in Healthcare and Education Topic Modeling Machine Learning in Materials Science Explainable Artificial Intelligence (XAI) Machine Learning in Healthcare |
| Papers published last year | ≈51 |
| ISSN | 3005-1460 |
| Citation impact (2-yr) | 3.25 |
| h-index | 7 |
| i10-index | 6 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | National University of Singapore |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such…
Abstract We review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific…
Recent advances in AI, especially vision-language foundation models (VLFMs), show promise in automating radiology report generation from complex 3D medical imaging data. Our review analyzes 23 studies on VLFMs, focusing on model architectures, capabilities, training datasets, and evaluation metrics. We discuss AI’s evolution in radiology, emphasizing the need for diverse datasets and standardized metrics, as…
We introduce PRefLexOR (Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning), a framework that integrates preference optimization with reinforcement learning (RL) concepts for self-improving scientific reasoning. PRefLexOR employs a recursive approach, refining intermediate steps before producing final outputs in training and inference. It optimizes log odds between preferred and non-preferred responses using an in-situ…
