JMIR AI

A new peer reviewed journal focused on research and applications for the health 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 

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


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.

Recent Articles

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Clinical Information and Decision Making

Imbalanced health care resource distribution has been central to unequal health outcomes and political tension around the world. Artificial intelligence (AI) has emerged as a promising tool for facilitating resource distribution, especially during emergencies. However, no comprehensive review exists on the use and ethics of AI in health care resource distribution.

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Viewpoints and Perspectives

Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms “Artificial intelligence,” “Machine learning,” and “regulation” from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper.

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Original Papers

Public health surveillance relies on the collection of data, often in near-real time. Recent advances in natural language processing make it possible to envisage an automated system for extracting information from electronic health records.

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Artificial Intelligence

Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement.

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Artificial Intelligence

Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable.

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Artificial Intelligence

Adolescence is a critical developmental period to prevent and treat the emergence of mental health problems. Smartphone-based conversational agents can deliver psychologically driven intervention and support, thus increasing psychological well-being over time.

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Original Papers

The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine.

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Editorial

JMIR AI is a new journal with a focus on publishing applied artificial intelligence and machine learning research. This editorial provides an overview of the primary objectives, the focus areas of the journal, and the types of articles that are within scope.

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Original Papers

Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears.

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Preprints Open for Peer-Review

There are no preprints available for open peer-review at this time. Please check back later.

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