JAMIA Open is a peer-reviewed, online-only, and Gold Open Access journal, JAMIA Open provides a global forum for the publication of novel research and insights in the major areas of informatics for biomedicine and health (e.g., translational bioinformatics, clinical research informatics, clinical informatics, public health informatics, and consumer health informatics), as well as related areas such as data science, qualitative research, and implementation science.
JAMIA Open articles, which include application notes, database notes, and patient/community perspectives, alongside original research, reflect the broad diversity of the field of informatics community, focusing on the intersection of informatics, health, communication, and technology, and how that intersection can support patient care through research, practice, and education. JAMIA Open authors are encouraged to make data and source code accessible through publicly accessible repositories that can be cited using digital object identifiers. Accepted manuscripts are required to have a patient/community facing abstract that highlights key findings. Author guidelines.
Neil Sarkar, PhD, MLIS, FACMI, is the Editor-in-Chief and leads a team of informatics leaders serving as the JAMIA Open Editorial Board and Associate Editors.
Access
JAMIA Open is an open access journal and does not require a login or AMIA membership to read articles.
Recent JAMIA Open Articles
Utilizing Immuno-Oncology registry data for enhanced non-small cell lung cancer treatment predictions.
August 1, 2025
We aim to leverage more comprehensive phenotypic and genotypic clinical data to enhance the treatment response predictions.
Large-scale deep learning for metastasis detection in pathology reports.
August 1, 2025
No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types.
A study of calibration as a measurement of trustworthiness of large language models in biomedical natural language processing.
August 1, 2025
To assess the calibration of 9 large language models (LLMs) within biomedical natural language processing (BioNLP) tasks, furthering understanding of trustworthiness and reliability in real-world settings.
An open-source, automated machine learning approach for large-scale image retrieval for thoracic aorta analysis studies.
August 1, 2025
To develop an image retrieval pipeline capable of identifying specific series of thoracic aortic computed tomography (CT) scans from a diverse database.