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Journal of Medical Economics Special Issue: Artificial Intelligence based medical devices and digital health for personalized medicine
About the special issue
The Editors of Journal of Medical Economics (Impact Factor: 2.956, CiteScore: 3.7) are planning a special issue titled ‘Artificial Intelligence based medical devices and digital health for personalized medicine’. Dr. Mihajlo Jakovljevic, AP Munjae Lee, Akihiko Ozaki and Prof João Paulo Ramos Teixeira are acting as guest advisors for the issue.
With the development of digital technology, the medical paradigm is changing from experience-based medicine to evidence-based medicine. This means that the medical provision method, which focused on post-mortem treatment in the past, is changing to the era of disease prevention and personalized medicine. In order to implement personalized medicine, data-centered medicine that incorporates digital technologies such as artificial intelligence and big data is emphasized, which must be accompanied by medical devices and digital health. In particular, the global COVID-19 pandemic is expanding the importance of medical devices and digital health, providing new healthcare services such as telemedicine and home health management.
Artificial intelligence-based medical devices and digital health refer to software that recognizes specific patterns by analyzing medical big data obtained from existing medical devices or patients, or medical devices with built-in software. We can provide customized medical care by diagnosing, treating, and predicting diseases through continuous performance improvement of artificial intelligence algorithms. Medical devices and digital health based on artificial intelligence, such as deep learning, big data analysis, and predictive modelling, are being developed, and although we want to provide customized medical care to patients, there are still limits to expanding their roles. Research is needed on the development of disease prediction models using artificial intelligence, the life cycle of artificial intelligence-based medical devices and digital health products, and the industrial ecosystem to which healthcare products can be applied.
This Special Issue focuses on medical device and digital health research and the development of disease prediction models using artificial intelligence. We aim to provide the latest technologies and new perspectives to related academic researchers and practitioners through convergence research to provide customized medical care. We welcome submission of original research, review, perspective, commentary and editorial articles on these themes, for details on how to write these article types see the instructions for authors.
Potential topics include but are not limited to the following:
Digital health blueprint
Development of a consumer-driven digital health model
Medical artificial intelligence predictive model
Machine learning-based disease prediction model
Real-world data, medical devices and digital health
Medical device life cycle
Evidence-Based Medicine and Systematic Literature Review
Future medicine and healthcare company
Medical system for digital health application
Community-based medical care and smart health
Digital Health and Medical Policy
Digital health – artificial intelligence and machine learning
The issue will be published in January 2024, I have included the last dates for submission below on the different publication tracks. Please note this journal is fully open access and operates under an article processing charge model, full details of this can be found here.
Submission to acceptance: 2 – 3 weeks
1 – 2 weeks for peer review
1 week for author revision
Acceptance to online publication: 1 – 2 weeks, with proofs within 5 working days and 48 hours for author review
Cost per article: $7000 / €6200 / £5500
Submission to acceptance: 5 – 6 weeks
3 – 4 weeks for peer review
2 weeks for author revision
Acceptance to online publication: 2 – 3 weeks, with proofs within 10 working days
Cost per article: $3900 / €3400 / £3000
The journal can offer waivers on fees, please get in touch with the Executive Editor, Mike Gregg, for more information.
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