Clinical AI & Digital Health
A Gold Open Access journal publishing peer-reviewed research at the intersection of computer science, medical devices, and clinical patient care.
About the Journal
Clinical applications of machine learning, digital health systems, and computational diagnostics.
Scholarly Open publishes highly relevant and FAIR-aligned research across our journals. We focus on transparent processes, rapid dissemination, and strong author support.
Scope & Coverage
- •Machine learning in medical imaging and diagnostics
- •Clinical decision support systems (CDSS)
- •Large language models (LLMs) in clinical workflows
- •Wearable devices and remote patient monitoring
- •Digital therapeutics and mobile health (mHealth)
- •Biomedical signal processing and telemetry
- •AI ethics, bias, and regulatory compliance in healthcare
- •Epidemiological modeling and public health informatics
Journal Sections
Editorial Board
Our distinguished editorial board members are leading experts in their fields, overseeing our rigorous peer review and ensuring high publication standards.
Editor-in-Chief
Position open
Under formation
Associate Editors
Join Our Editorial Team
We are always looking for distinguished scholars to join our editorial board. If you are interested in contributing to the advancement of open access, we would love to hear from you.
Articles
Explore the latest high-impact peer-reviewed research articles published open-access in this journal.
Sample Articles
These article outlines are illustrative examples for our launch journals.
This study validates a deep learning diagnostic system for mammography screening, showing robust sensitivity and specificity across multicenter datasets.
We evaluate the impact of LLM-assisted documentation in EHR systems, showing substantial reductions in administrative burden and high clinician satisfaction.
Submit to Clinical AI & Digital Health
Please review our author guidelines and prepare your manuscript according to our submission requirements. We welcome original research, reviews, and methodological contributions that support FAIR scholarship.