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

#PREreview Live Review Report

Peer Review of “Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance”

#Live Reviewed Article Collection

Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance

Background: Rural healthcare providers face unique challenges such as limited specialist access and high patient volumes, making accurate diagnostic support tools essential. Large language models (LLMs) like GPT-3 have demonstrated potential in clinical decision support but remain understudied in pediatric differential diagnosis.

Authors' Responses to Peer-Reviews

Authors’ Response to Peer Reviews of “Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance”

Peer Reviews

Peer Review of “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection”

#xHealthInformatics

Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection

The increasing integration of artificial intelligence systems into critical societal sectors has created an urgent demand for robust privacy-preserving methods. Traditional approaches such as differential privacy and homomorphic encryption often struggle to maintain an effective balance between protecting sensitive information and preserving data utility for AI applications. This challenge has become particularly acute as organizations must comply with evolving AI governance frameworks while maintaining the effectiveness of their AI systems.

Peer Reviews

Peer Review of “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection”

Authors' Responses to Peer-Reviews

Authors’ Response to Peer Reviews of “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection”

Authors' Responses to Peer-Reviews

Authors’ Response to Peer Reviews of “Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development”

This is the author(s)’ responses to peer review reports related to MS ID 57719

#xPediatrics

Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development

Pneumonia is a leading cause of mortality in children under five. While machine learning (ML) has been applied to pneumonia diagnostics, few studies have focused on predicting the need for escalation of care in pediatric cases. This study aims to develop an ML-based clinical decision support tool for predicting the need for escalation of care in community-acquired pneumonia (CAP) cases.