## 4 Discussion  
In the scope of this study, we conceptualized and tested two different search methods to assist SMEs with the retrieval and screening process of relevant clinical data for medical products for subsequent decision support and as a basis for future systematic reviews. The developed semantic BERT-based search method and term-oriented MeSH-based search method were evaluated in a use case setting with two SMEs on the databases PubMed and ClinicalTrials.gov as well as with the established CLEF 2018 eHealth TAR dataset. The results of our SME use case evaluation show that the goal to conceptualize resource-efficient and effective retrieval methods to assist SMEs was achieved. Both search methods retrieved numerous relevant clinical trials and publications within the top 10 search results. In total, 47 out of 80 retrieved publications and 17 out of 40 retrieved clinical trials were relevant for both SMEs and identified by screening a relatively low number of documents. To refine the search, short 30-min feedback sessions in accordance with the SMEs were used to evaluate the results while respecting the limited SME resources. The resulting overall precision of 33.3% for the MeSH-based method and 73.3% for the BERT-based method is much higher compared to the precision of previous studies with systematic reviews in the scope of the MDR certification. In one recent systematic review, 60 out of 413 retrieved publications were deemed relevant based on the title and abstract, resulting in a precision of 14.5% [9]. In another systematic review for a medical product for the MDR certification, only 21 publications out of 242 retrieved publications were deemed relevant after screening, resulting in an even lower precision of 8.7% [25]. One reason for the difference in performance could be the notably smaller sample size of 40 publications and 20 clinical trials as well as the differing scope of initial scoping searches in our SME use cases instead of systematic reviews of the compared studies. While respecting these structural limitations, the higher overall precision of our search methods indicates the potential to retrieve a comparatively large number of relevant documents while screening much fewer documents compared to manual literature searches. This indication aligns with recent studies stating an approximate reduction of 50% in papers to screen via automated literature screening methods [45]. However, depending on the type of underlying study or task of the literature screening, the reduced workload can vary between 7% to 71% while maintaining a high level of recall [46]. A high recall was also observed during our second evaluation of both search methods with the CLEF 2018 eHealth TAR dataset, in which both methods achieved a high recall in a more general, nonspecific medical device, setting. While the BERT-based method reached an overall recall of 71.8%, the MeSH-based method outperformed most other methods with a recall of 86.4%. In particular, the new weighting of MeSH terms based on the feedback loops with an increasing number of positive seed documents led to the high performance of the MeSH-based search. However, despite the high performance, attention should be paid to the generalizability of the search. With the currently implemented weighting scheme, terms occurring frequently in relevant documents have a higher weight and focus the search more on a specific field. Underrepresented fields of possibly equal relevance may be excluded, potentially resulting in data bias in the search, also referred to as hasty generalization [47; 48]. Moreover, while the inclusion of negative seed documents from the SME feedback loops led to reasonable results in the MeSH-based search, future use cases might require different sampling strategies to balance relevant and irrelevant seeds and avoid potential data bias [49; 50]. Another potential bias could occur when searching for information relevant to highly innovative medical products.