Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)–based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems. Key Points • Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration. • The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems. • While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns.
Research and development (R&D) in many technological areas is characterized by growing complexity. In biomedical engineering, too, interdisciplinary collaboration is regarded as a promising way to master this challenge. Therefore, identifying suitable experts becomes crucial, which is currently being researched, amongst others, by analyzing semantic data. However, previous approaches lack clarity and traceability of the mechanisms for compiling top-n lists of recommended experts, as domain specificity in profiling is insufficient. Moreover, these recommenders are mainly based on scientific publications, while patents are rarely considered as an important outcome of R&D. Thus, we study the feasibility of profiling 16 biomedical engineering experts using both publications and patents. These documents are automatically labeled according to a three-dimensional domain model by machine learning-based classifiers. On this basis, we created various activity-based representations, including author-contributionweighting. We evaluated the profiling through self-and external-assessments and tested the recommendation compared to scientometric measures in three case studies. All interviewed experts identify themselves among 10 pseudonymous profiles and 96% of all 51 externalassignments are correct. The recommendation over three case studies reaches a high mean average precision of 89% and contrasts with the use of scientometric measures (41%). Moreover, the activity based on patents primarily corresponds to that of publications but patents also introduce new activities. The author-contribution-weighting improves the performance. In conclusion, our findings show that exploiting publications and patents enables comprehensible profiling of biomedical engineering experts that allows visual comparisons and clear selection and ranking of potential R&D collaboration partners along the translational value chain.
The pathway from the flash of a technological invention until its use as a medical device in every day care is tedious and burdensome. But the often postulated acceleration has to balance the speed of innovation and the indispensable product safety by an improved understanding of the innovation cycle. While several studies investigated the time course of pharmaceutical innovation, a comparable empirical analysis of medical devices is lacking. Thus we evaluated the time between the patent priority date and the corresponding receipt of the CE mark as a function of a medical device risk class in 61 cases. The statistical analysis yielded a time increment (trend) from medical devices in risk category I (median = 5.8 years) compared to risk category III (median = 10.4 years), which is close to literature reported values for drug development (9–12 years). The difference between products in risk classes I and II did not reach significance. To investigate the underlying facts, a text-mining approach especially to resolve the ambiguity of, e.g. patents, CE Marks etc. is suggested for increasing the sample size.
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