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. 

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.

Artificial intelligence is rapidly shifting from experimental code to point-of-care medical decision-making. As machine learning models gain autonomy in diagnosing diseases and tailoring treatments, ensuring true patient autonomy through informed consent has become a vital ethical challenge.

Postpartum hemorrhage (PPH) remains a leading cause of maternal morbidity worldwide, with blood transfusion rates hovering around 2% to 2.7%. While discussing these risks is a vital component of antenatal informed consent and shared decision-making, translating historical medical records into an accurate personal risk profile is notoriously difficult.

The rapid rise of generative artificial intelligence (GenAI) has created powerful new instructional opportunities alongside major pedagogical questions. While tools like ChatGPT can enhance lesson planning, case scenario design, and assessment development, many medical educators face a steep learning curve. Without structured guidance, integrating these fast-evolving and context-sensitive tools into high-stakes, discipline-specific medical curricula carries significant risks.

Public health emergencies demand synthesized evidence rapidly. During COVID-19, Ebola outbreaks, and other crises, researchers face pressure to produce guidance within the first week hours, for example, to prevent further spread of infections in the community. Meanwhile, traditional systematic reviews require 6 to 18 months to complete. This temporal mismatch creates an evidence vacuum that decision-makers often fill with preliminary findings or incomplete data.

Artificial intelligence is fast becoming the digital front door of modern medicine. In the face of overwhelming patient demand, healthcare systems are rapidly deploying AI-enabled triage tools to sort symptoms, assess risk, and route patients before they ever step into a clinic.