JMIR AI

A new peer reviewed journal focused on research and applications for the health artificial intelligence (AI) community.

Editor-in-Chief:

Khaled El Emam, PhD,  Canada Research Chair in Medical AI, University of Ottawa; Senior Scientist, Children’s Hospital of Eastern Ontario Research Institute: Professor, School of Epidemiology and Public Health, University of Ottawa, Canada

Bradley Malin, PhD, Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science; Vice Chair for Research Affairs, Department of Biomedical Informatics: Affiliated Faculty, Center for Biomedical Ethics & Society, Vanderbilt University Medical Center, Nashville, Tennessee, USA


JMIR AI is a new journal that focuses on the applications of AI in health settings. This includes contemporary developments as well as historical examples, with an emphasis on sound methodological evaluations of AI techniques and authoritative analyses. It is intended to be the main source of reliable information for health informatics professionals to learn about how AI techniques can be applied and evaluated. 

JMIR AI is indexed in DOAJ, PubMed and PubMed Central.

 

Recent Articles

Article Thumbnail
Viewpoints and Perspectives in AI

Ambient scribe technology, utilizing large language models, represents an opportunity for addressing several current pain points in the delivery of primary care. We explore the evolution of ambient scribes and their current use in primary care. We discuss the suitability of primary care for ambient scribe integration, considering the varied nature of patient presentations and the emphasis on comprehensive care. We also propose the stages of maturation in the use of ambient scribes in primary care and their impact on care delivery. Finally, we call for focused research on safety, bias, patient impact, and privacy in ambient scribe technology, emphasizing the need for early training and education of health care providers in artificial intelligence and digital health tools.

|
Article Thumbnail
Applications of AI

Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are models that enable representing words as vectors and are the building block of most natural language processing systems. If word embeddings are trained on clinical trial abstracts, predictive models that use the embeddings will exhibit gender performance gaps.

|
Article Thumbnail
Applications of AI

Physicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language processing–assisted clinical documentation tools may provide a solution.

|
Article Thumbnail
Applications of AI

Hypertension is the most common reason for postpartum hospital readmission. Better prediction of postpartum readmission will improve the health care of patients. These models will allow better use of resources and decrease health care costs.

|
Article Thumbnail
Foundations of AI

A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening.

|
Article Thumbnail
Applications of AI

Collecting information on adverse events following immunization from as many sources as possible is critical for promptly identifying potential safety concerns and taking appropriate actions. Febrile convulsions are recognized as an important potential reaction to vaccination in children aged <6 years.

|
Article Thumbnail
Foundations of AI

Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In this paper, we focus on COPD in the United States from 2016 to 2019.

|
Article Thumbnail
Foundations of AI

Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints.

|
Article Thumbnail
Reviews in AI

The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care.

|
Article Thumbnail
Foundations of AI

Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, little is known about the fairness and bias of these predictive models.

|
Article Thumbnail
Reviews in AI

Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people’s health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated.

|
Article Thumbnail
Applications of AI

With the rapid evolution of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT-4 (OpenAI), there is an increasing interest in their potential to assist in scholarly tasks, including conducting literature reviews. However, the efficacy of AI-generated reviews compared with traditional human-led approaches remains underexplored.

|

Preprints Open for Peer-Review

We are working in partnership with