## 1 Introduction  
To increase patient safety in Europe, the regulatory framework Medical Device Regulation (MDR) was adopted in 2017 and became applicable in May 2021 [1]. The novel MDR forces medical device manufacturers worldwide to comply quickly with the drastically increased requirements for European market access regarding post-market surveillance [2], medical device traceability [2], and more rigorous pre-market testing [3]. Moreover, new rules for the risk classification of medical devices cause previously lower-classified devices to shift to a higher-risk class [3]. These requirements and rules increase the burden on companies to obtain approval for innovative medical products. Consequently, medical device suppliers and manufacturers may disappear from the market. Small and medium-sized enterprises (SMEs) are assumed to be especially at risk when facing the extended time-to-market [4, 5]. The demor startups could lead to a loss of innovative power and supports the formation of an oligopoly of larger companies [4]. One of the most crucial aspects of adapting to the MDR is the clinical evaluation throughout the whole product life cycle, starting from the initial certification until the post-market clinical follow-up (PMCF). The associated clinical evaluation report requires the systematic collection and evaluation of relevant clinical data [6]. This acquisition and unbiased review of clinical data in the form of scientific literature or clinical trials is now mandatory across all risk classes [3; 7]. Not only clinical data pertinent to the respective medical device but also available alternative treatment options for its purpose must be considered [8]. Alternatively, it is possible to demonstrate equivalence to an already certified medical device under the MDR [9], using published data instead of conducting clinical trials. Even before an approval process is initiated, a valid picture of the existing clinical knowledge based on an unbiased review is necessary to decide how to proceed. At this stage, automation of the review process is both possible and necessary to accelerate development. Methods to automate such review processes were evaluated in the scope of the Conference and Labs of Evaluation Forum (CLEF) with the so-called eHealth challenges regarding Technology Assisted Reviews (TAR) for systematic reviews (SR) in Empirical Medicine [10], in which relevant documents must be automatically retrieved for a given topic. The best-performing method achieved an almost perfect overall recall of relevant documents, while the recall regarding the first search results and consequently the workload reduction could be optimized further [11]. One method to improve recall at earlier stages of the screening process could be to use terms from the Medical Subject Headings (MeSH), used by PubMed for indexing publications. The application of MeSH terms helps with maximizing recall and formulating more effective search queries [12]. Since the selection of suitable MeSH terms poses a difficult task even for experts, automated selection strategies exist [13; 14]. However, limiting the search to the most appropriate terms leads to a loss of information and thus to a reduction in the retrieved relevant documents. To overcome the need of forming queries by selecting suitable keywords and Boolean operators, recent methods explored the possibility of a semantic search, rather than a term-based search [15]. In the current research, semantic search applications via Bidirectional Encoder Representations from Transformers (BERT) language model variations perform well and are used to retrieve domain-specific information [16; 17] or to retrieve relevant documents for TARs [18]. While the performant semantic BERT-based search is not restricted by inherent limitations of keyword-based searches, the problem of transparency and understandability of the search arises and is currently being researched [19; 20]. Thus, it is currently difficult to interpret and understand the results of a BERT-based search.