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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File: | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:GA Hauschild iScience2022 25-12.jpg|240px]]</div> | ||
'''"[[Journal: | '''"[[Journal:Guideline for software life cycle in health informatics|Guideline for software life cycle in health informatics]]"''' | ||
The | The long-lasting trend of [[medical informatics]] is to adapt novel technologies in the medical context. In particular, incorporating [[artificial intelligence]] (AI) to support clinical decision-making can significantly improve monitoring, diagnostics, and prognostics for the patient’s and medic’s sake. However, obstacles hinder a timely technology transfer from the medical research setting to the actual clinical setting. Due to the pressure for novelty in the [[research]] context, projects rarely implement [[Quality (business)|quality]] standards. Here, we propose a guideline for academic software life cycle (SLC) processes tailored to the needs and capabilities of research organizations ... ('''[[Journal:Guideline for software life cycle in health informatics|Full article...]]''')<br /> | ||
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Revision as of 15:18, 23 October 2023
"Guideline for software life cycle in health informatics"
The long-lasting trend of medical informatics is to adapt novel technologies in the medical context. In particular, incorporating artificial intelligence (AI) to support clinical decision-making can significantly improve monitoring, diagnostics, and prognostics for the patient’s and medic’s sake. However, obstacles hinder a timely technology transfer from the medical research setting to the actual clinical setting. Due to the pressure for novelty in the research context, projects rarely implement quality standards. Here, we propose a guideline for academic software life cycle (SLC) processes tailored to the needs and capabilities of research organizations ... (Full article...)
Recently featured: