The "Journal of Medical Internet Research" (JMIR; ISSN 1438-8871, Medline-abbreviation: J Med Internet Res) (founded in 1999) is a leading health informatics and health services/health policy journal (ranking in the first quartile Q1 by Impact Factor in these disciplines) focusing on digital health, data science, health informatics and emerging technologies for health, medicine, and biomedical research. The journal is ranked #1 on Google Scholar in the 'Medical Informatics' discipline.

JMIR was the first open access journal covering health informatics, and the first international scientific peer-reviewed journal on all aspects of research, information and communication in the healthcare field using Internet and Internet-related technologies; a broad field, which is known as "eHealth" [see also What is eHealth and What is eHealth (2)], or now also "digital health", which includes mHealth (mobile health). This field also has significant overlaps with what is called "consumer health informatics", health 2.0/medicine 2.0, or participatory medicine. This focus makes JMIR unique among other medical or medical informatics journals, which tend to focus on clinical informatics or clinical applications. As eHealth/mHealth is a highly interdisciplinary field we are not only inviting research papers from the medical sciences, but also from the computer, behavioral, social and communication sciences, psychology, library sciences, informatics, human-computer interaction studies, and related fields.

The term "Internet" is used in its broadest sense, so we are also interested in high impact studies and applications of digital medicine, mobile technologies, social media, novel wearable devices and sensors, connected home appliances, domotics etc.

The journal invites manuscripts that deal with the following topics (the main themes/topics covered by this journal and sample papers can also be found here):

novel digital health approaches, methods, and devices
large digital medicine / digital therapeutics trials with clinical impact
data science, open data
studies evaluating the impact of Internet/social media use or specific eHealth/mHealth interventions on individual health-related or social outcomes
evaluations and implementations of innovative mhealth (mobile health) applications, social media apps, ubiquitous computing, or innovative and emerging technologies in health
descriptions of the design and impact of Internet and mobile applications and websites or social media for consumers/patients or medical professionals
use of the Internet, social media and mhealth in the context of clinical information and communication, including telemedicine
use of the Internet, social media, and mhealth in medical research and the basic sciences such as molecular biology or chemistry (e.g. bioinformatics, online factual databases)
medical information management and librarian sciences
e-learning and knowledge translation, online-courses, social media, web-based and mobile programs for undergraduate and continuing education,
eHealth/mHealth and social media applications for public health and population health technology (disease monitoring, teleprevention, teleepidemiology)
evidence-based medicine and the Internet and mhealth (e.g. online development or dissemination of clinical guidelines, measuring agreement about management of a given clinical problem among physicians, etc.)
the impact of eHealth/mHealth/pHealth/iHealth, social media, the Internet, or health care technologies on public health, the health care system and policy
methodological aspects of doing Internet/mhealth/social media research, e.g. methodology of web-based surveys
design and validation of novel web-based instruments
ecological momentary assessment, sensors, mobile technologies for gathering and analyzing data in real-time
analysis of e-communities, social media communities, or virtual social networks
comparisons of effectiveness of health communication and information on the Internet/mHealth/social media compared with other methods of health communication,
effects of the Internet/mhealth/social media and information/communication technology on the patient-physician relationship and impact on public health, e.g. the studies investigating how the patient-physician relationship changes as a result of the new ways of getting medical information
ethical and legal problems as well as cross-border and cross-cultural issues of eHealth/mHealth
systematic studies examining the quality of medical information available in various online venues
methods of evaluation, quality assessment and improvement of Internet information or eHealth applications
proposals for standards in the field of medical publishing on the Internet, including self-regulation issues, policies and guidelines to provide reliable healthcare information
results and methodological aspects of Internet-based and social media studies, including medical surveys, psychological tests, quality-of-life studies, gathering and/or disseminating epidemiological data, use of the Internet/mobile apps/social media for clinical studies (e-trials), drug reaction reporting and surveillance systems etc.
electronic medical publishing, Open Access publishing, altmetrics, and use of the Internet or social media for scholarly publishing (e.g. collaborative peer review)
information needs of patients, consumers and health professionals, including studies evaluating search and retrieval behavior of patients
web-based studies, e.g. online psychological experiments
evaluations of mhealth (mobile) applications, as well as ambient / ubiquitous computing approaches, sensors, domotics, and other cutting edge technologies
personal health records, patient portals, consumer health informatics applications
behavior change technologies
Reviews, viewpoint papers and commentaries touching on the issues and themes listed above are also welcome, but should be grounded in data and/or a thorough literature review

In addition, the Journal will occasionally publish original research, reviews and tutorials on more generic, related topics such as:

Internet standards
cybermetrics
security and confidentiality issues
Internet demographics
social impact of the Internet
digital imaging and multimedia
health care records
high-speed networks
telecommunication
electronic publishing
software development

The Journal of Medical Internet Research is one of the flagship journals of JMIR Publications and is highly selective. We are not a megajournal that publishes everything regardless of impact. To ensure a rapid turnaround time, we encourage that authors consider other JMIR journal titles as well. While it is possible to transfer submissions from one journal to another before, during or after the review process (based on editorial suggestions), authors can avoid delays in decision-making by submitting to the right journal.

In order to be considered for J Med Internet Res, clinical informatics papers should have a clear connections to the major themes in this journal of consumer/patient empowerment and participatory healthcare, and/or evaluate the use of mobile/Internet-based/emerging technologies such as patient portals. Other clinical informatics studies with no relationship to consumer health informatics, or more technical papers are best submitted to other JMIR journal titles, such as Interactive Journal of Medical Research (i-JMR, a general medical journal with focus on innovation), JMIR mHealth and uHealth, JMIR Medical Informatics, or JMIR Human Factors.

Machine-learning papers: Machine learning papers are now mostly published in JMIR Medical Informatics (see e-collection Machine Learning), JMIR Formative Research or JMIR AI, or another sister journal, unless they have reached clinical maturity and are being used and validated in routine clinical use. Our flagship journal J Med Internet Res no longer publishes ML papers unless 1) they show a direct clinical effect or impact on care, 2) are validated using an independent dataset not used for training, 3) are written in a language that can be understood by a healthcare professional, and provide open source or a publicly available tool that can be used by others to validate or apply the findings. We also request that 4) reporting strictly adheres to the "Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research". Highly technical papers (with mathematical formulas) are unsuitable for J Med Internet Res or this information needs to be provided in a Multimedia Appendix.

Digital psychiatry and digital mental health papers are best suited for JMIR Mental Health if they are impactful, otherwise JMIR Formative Research publishes early stage work.

Studies related to public health informatics and surveillance systems should preferably be submitted to JMIR Public Health & Surveillance. JPHS is also highly selective.

Papers with focus on games in health or gamification aspects of apps and theoretical issues/commentary on gaming are now primarily published in / transferred to JMIR Serious Games.

Studies evaluating systematically the quality of health information or present tools for social listening may be best suited for JMIR Infodemiology.

Formative work such as usability studies, pilot studies, and feasibility studies are no longer published in our flagship journals and should be submitted to JMIR Formative Research.

Protocols and proposals can be submitted to JMIR Research Protocols.

Submitted manuscripts are subject to a rigorous but speedy peer review process. We aim for a standard review time of less than 2 months, and a review time of 4 weeks for submission to initial decision for fast-tracked papers).

The review process is designed to help authors to improve their manuscripts by giving them constructive comments on how to improve their paper, and to publish only those articles which comply to general quality criteria of a scholarly paper, especially originality, clarity, references to related work and validity of results and conclusions.

This paper is in the following e-collection/theme issue:

Authors’ Reply: Foundation Models for Generative AI in Time-Series Forecasting

Authors’ Reply: Foundation Models for Generative AI in Time-Series Forecasting