Ask any peer reviewer or editor what's changed most about their workload in the last two years, and generative artificial intelligence (AI) is likely to come up quickly in conversation. Peer reviewers might identify AI tools as something they've started using for extra review help, or they’re thinking about whether to do this and how to do it ethically. Editors will say they’re seeing more and more formulaic-appearing peer reviews that look suspiciously like they are AI-generated. Authors also are noticing this in peer reviews too. 

Generative artificial intelligence (AI) tools can genuinely help researchers organize, conduct, and write up their research work faster than ever before. However, a lack of transparency on how these tools were applied and their outputs verified can influence scientific work and its trustworthiness. 

Why does anyone agree to review a manuscript at all? It's unpaid, mostly invisible work, on top of a reviewer's own research and clinical or academic responsibilities. Yet the entire scholarly publishing system depends on people saying yes. I work on peer review operations and editorial policy at JMIR Publications, and this is the question I keep coming back to.

Peer review is the heartbeat of scholarly publishing. It requires countless unseen hours of critical reading, deep intellectual effort, and constructive feedback to ensure published research is rigorous, accurate, and trustworthy. Yet, despite being essential to scientific progress, the dedicated effort behind peer review often goes unacknowledged.

Sustaining peer review capacity amid rising submission volumes requires balancing three core elements: speed, quality, and expanding the reviewer workforce. By treating reviewer feedback as a collaborative editing process rather than a barrier, incorporating vetted early-career researchers into the reviewer pool, and utilizing transparent review models, the scientific community can drastically reduce publication bottlenecks without compromising integrity.

African American and Black women report the lowest physical activity levels across demographic groups in the United States, driving elevated rates of preventable cardiometabolic diseases. While researchers increasingly turn to mixed methods to examine how social, cultural, and environmental factors shape exercise behaviors, how effectively are these quantitative and qualitative insights being combined?

Heart rate variability (HRV), the fluctuation in time intervals between consecutive heartbeats, has emerged as a key noninvasive indicator of stress, recovery, and overall cardiovascular health. With smartphone cameras and wearable sensors expanding remote monitoring capabilities, consumer access to HRV tracking has skyrocketed.

In low-resource settings, access to timely emergency trauma care is often a matter of life and death. In Uganda, traffic law enforcement officers and medical trainees frequently serve as the initial first responders and frontline caregivers for road traffic injuries, particularly those involving motorcycles.

Generative artificial intelligence is rapidly transforming scientific research, raising critical questions about how journals safeguard research integrity. A study published in the MIT Science Policy Review audited AI policies across global medical journals and called for a major shift—moving from static rules that merely police authors to an active, ongoing stewardship of the scientific record.

In this independent evaluation, JMIR Publications was recognized alongside JAMA and PLOS for operating at an AI Governance Readiness Level of 2, the highest level achieved amongst the journals evaluated  in the study sample.

As artificial intelligence rapidly transforms clinical workflows, medical educators face a unique dilemma: How do we prepare trainees for an AI-driven future without undermining their core diagnostic skills?  A recent piece from the Yale School of Medicine caught our eye because it directly explores this double-edged sword. Written by Serena Crawford, Associate Communications Director at Yale School of Medicine, the Q&A highlights the pioneering work of Jaideep Talwalkar, MD, Associate Dean for Educational Technology and Innovation, as he examines how ambient AI scribes impact medical students.