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BioMedInformatics is a peer-reviewed journal published by MDPI, covering AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Artificial Intelligence in Healthcare and Education. Over its lifetime it has published 373 papers, which have received 3,355 citations.
| Publisher | MDPI |
|---|---|
| Reference style | Numbered (MDPI) Numbered — [1], [2] in the text 1. Smith, A.; Jones, B.; Lee, C. A Representative Article Title. BioMedInformatics 2023, 12, 45–58.
Formats any DOI in BioMedInformatics style. No sign-up. |
| Publishes research in | AI in cancer detection Radiomics and Machine Learning in Medical Imaging Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare COVID-19 diagnosis using AI |
| ISSN | 2673-7426 |
| Citation impact (2-yr) | 6.59 |
| h-index | 26 |
| i10-index | 103 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| 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.
The lack of consistent presentation of results in published studies on the association between a quantitative explanatory variable and a quantitative dependent variable has been a long-term issue in evaluating the reported findings. Studies are analyzed and reported in a variety of ways. The main purpose of this review is to illustrate the procedures in…
The use of artificial intelligence (AI) systems in biomedical and clinical settings can disrupt the traditional doctor–patient relationship, which is based on trust and transparency in medical advice and therapeutic decisions. When the diagnosis or selection of a therapy is no longer made solely by the physician, but to a significant extent by a machine…
Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung cancer detection, highlighting key advances,…
Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly emerging pathogens. Rapid identification, epidemiological surveillance, and transmission mitigation are crucial challenges…
Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be…
