Hauschild Anne-Christin, Eick Lisa, Wienbeck Joachim, Heider Dominik
Department of Data Science in Biomedicine, Faculty of Mathematics & Computer Science, Philipps University of Marburg, Hans-Meerwein-Strasse 6, Marburg, 35032, Germany.
iScience. 2021 Jul 1;24(7):102803. doi: 10.1016/j.isci.2021.102803. eCollection 2021 Jul 23.
Computational methods can transform healthcare. In particular, health informatics with artificial intelligence has shown tremendous potential when applied in various fields of medical research and has opened a new era for precision medicine. The development of reusable biomedical software for research or clinical practice is time-consuming and requires rigorous compliance with quality requirements as defined by international standards. However, research projects rarely implement such measures, hindering smooth technology transfer into the research community or manufacturers as well as reproducibility and reusability. Here, we present a guideline for quality management systems (QMS) for academic organizations incorporating the essential components while confining the requirements to an easily manageable effort. It provides a starting point to implement a QMS tailored to specific needs effortlessly and greatly facilitates technology transfer in a controlled manner, thereby supporting reproducibility and reusability. Ultimately, the emerging standardized workflows can pave the way for an accelerated deployment in clinical practice.
计算方法能够变革医疗保健领域。特别是，结合人工智能的健康信息学在应用于医学研究的各个领域时展现出了巨大潜力，并开启了精准医学的新时代。开发用于研究或临床实践的可重复使用生物医学软件耗时且需要严格遵守国际标准所定义的质量要求。然而，研究项目很少实施此类措施，这阻碍了技术顺利向研究团体或制造商转移，也影响了研究的可重复性和软件的可再利用性。在此，我们提出了一个针对学术机构的质量管理体系（QMS）指南，该指南纳入了基本要素，同时将要求限制在易于管理的范围内。它提供了一个起点，能让人们轻松地实施针对特定需求的质量管理体系，并极大地促进以可控方式进行技术转移，从而支持研究的可重复性和软件的可再利用性。最终，新兴的标准化工作流程可为在临床实践中加速部署铺平道路。
