Advances in Medical Informatics to Improve Health Care
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".
Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 7093
Special Issue Editor
Special Issue Information
Dear Colleagues,
Digital transformation has reached all branches of industry and society, including health care and biomedical sciences. Buzzwords such as Big Data and Artificial Intelligence (AI), including Machine and Deep Learning, eHealth, digital health, digital medicine, or medical data science, are used interchangeably to highlight opportunities to promote patients’ health and quality of life by preventing diseases, reducing disease burden, and improving effective diagnostics or treatments. However, in order to fully utilize intelligent information processing for improved decision-making, many key challenges need to be addressed, such as high-quality data capture and integration, interoperability of systems, AI explainability, regulatory requirements regarding data protection, IT security and medical device approval, and last but not least, evidence of improved decision making, patient outcomes, or resource management.
Advances in Medical Informatics are key drivers of digital transformation in medicine, providing a foundation to establish sustainable research infrastructures, algorithms, tools, and standards for health care and medical research—including patient engagement—which will address the aforementioned challenges. Original research papers, software papers, reviews, or letters from different disciplines, including—but not limited to—digital health or biomedical informatics approaches, especially those addressing key challenges, are welcome for this Special Issue.
Prof. Dr. Julian Varghese
Guest Editor
Manuscript Submission Information
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Keywords
- medical informatics
- artificial intelligence in medicine
- interoperability
- precision medicine
- data integration
- data management
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