JMIRx Med

PubMed-indexed overlay journal for preprints with post-review manuscript marketplace (What is JMIRx?).

Editor-in-Chief:

Edward Meinert, MA (Oxon), MSc, MBA, MPA, MPH, PhD, CEng, FBCS, EUR ING, Professor of Digital Health and Clinical Artificial Intelligence, Newcastle University, United Kingdom

JMIRx Med (ISSN 2563-6316), which has been accepted for indexing in PubMed and PubMed Central, is an innovative overlay journal to MedRxiv and JMIR Preprints (other preprint servers are invited to join). JMIRx peer-reviews preprints and publishes their revised "version of record" with peer-review reports across a broad range of medical, clinical and related health sciences. Unlike the majority of JMIR journals, papers published in this journal do not require a digital health focus - in fact, most papers we published in the first months of the journal were related to COVID19, but we publish all research that qualifies for preprinting on MedRxiv.

Conceived to address the urgent need to make highly relevant scientific information available as early as possible without losing the quality of the peer-reviewed process, this innovative new journal is the first in a new series of “superjournals”. Superjournals (a type of "overlay" journal) sit on top of preprint servers (JMIRx-Med serves MedRxiv and JMIR Preprints), offering peer-review and everything else a traditional scholarly journal does. Our goal is to rapidly peer review and publish a paper. All JMIRx Med papers must have originated as a preprint.

All JMIRx Med papers are rigorously peer-reviewed, copyedited and XML-tagged. Accepted papers are published along with the related Peer Review Reports and Author Responses to Peer Review Reports, providing an additional layer of transparency to the scholarly publishing process.

There is no Article Processing Fee directly paid by authors for this journal. JMIRx Med is envisioned as a diamond open access and Plan-P compliant journal, which enables Plan P member universities/institutions and funders to subsidize peer review of preprints and publishing in JMIRx Med. Individual PI-led labs, departments and universities can become institutional members, guaranteeing unlimited peer-review of preprints.

If you are not affiliated with a Plan P member organization, we encourage you to provide Plan P membership details to your administrator or sign up for a Principal Investigator (PI) level membership. Further details provided here.

For a limited time only, authors who opt-in during submission to receive PREreview or PeerRef community peer review for their preprint or refer us to their department head/librarian/funder contact will receive a membership-waiver and may publish the preprint in JMIRx Med at no cost to the author. Referral form provided here.

To submit a preprint to JMIRx, authors can self-nominate their existing preprints for publication (which is the equivalent to a traditional journal submission), using the minimalistic JMIRx-Med submission form that essentially only points to the preprint (the preprint needs to be unpublished and should not be under consideration by a journal).

Preprints that have already been peer-reviewed by third-party Plan P accredited peer-review services such as PREreview and PeerRef do not require further peer-review (at the editors' discretion). In the submission process, you can nominate your preprint for a PREreview journal club, which can be used in lieu of traditional peer-review.

For more details on other submission pathways (including for papers not in MedRxiv) and peer-review options see How to submit to a JMIRx journal.

For more information on JMIRx please also see our Knowledge Base article "What is JMIRx?".

Recent Articles

Authors' Responses to Peer-Reviews

Author’s Response to Peer Reviews of “Real-Time Health Monitoring Using 5G Networks: Deep Learning–Based Architecture for Remote Patient Care”

This is the author(s)' response to peer review reports related to "Real-Time Health Monitoring Using 5G Networks: A Deep Learning Based Architecture for Remote Patient Care"

Peer Reviews

Peer Review of “Real-Time Health Monitoring Using 5G Networks: Deep Learning–Based Architecture for Remote Patient Care”

This is a peer review report related to "Real-Time Health Monitoring Using 5G Networks: A Deep Learning Based Architecture for Remote Patient Care"

#xHealthInformatics

Real-Time Health Monitoring Using 5G Networks: Deep Learning–Based Architecture for Remote Patient Care

Remote patient monitoring systems face critical challenges in real-time vital sign analysis and secure data transmission.

Peer Reviews

Peer-Review of “Real-Time Health Monitoring Using 5G Networks: Deep Learning–Based Architecture for Remote Patient Care”

This is a peer review report related to "Real-Time Health Monitoring Using 5G Networks: A Deep Learning Based Architecture for Remote Patient Care"

#xInfectiousDiseases

COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning–Based Transfer Learning Approach

SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants [1]. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has risen to 11% [1], marking the highest level in over six months despite widespread immunization efforts [2]. Newer variants demonstrate enhanced host cell binding, increasing both infectivity and diagnostic complexity.

Peer Reviews

Peer Review of “COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach”

This is a peer review report related to "COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach"

Peer Reviews

Peer Review of “COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach”

This is a peer review report related to "COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach"

Authors' Responses to Peer-Reviews

Author’s Response to Peer Reviews of “COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach”

This is the author(s)' response to peer review reports related to "COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach"