## 4 Discussion
While the MeSH-based method could extract existing MeSH terms for fundamental information, such as the intended purpose of the device, target group, and clinical benefits even for highly innovative products, MeSH terms relevant to aspects such as the innovative technologies may be lacking. It is possible that there are not any relevant existing MeSH terms since MeSH terms for emerging fields might have yet tobe added to the terminology. The BERT-based method, in contrast, might not feature such a bias regarding innovative products since semantics in the product description and thus similar technologies mentioned in literature can still be retrieved despite a lack of relevant MeSH terms. Future use cases, however, could investigate the potential bias when working with highly innovative medical devices. Hence, the novelty of medical products is one possible reason for the differences regarding as relevant rated MeSH terms for medical products by SMEs as observed in Table 2. A second possible reason for the differences could be differing product types. Only a few MeSH terms might describe highly specialized medical products, whereas multiple MeSH terms might describe medical products with multiple technologies or intended purposes. A third possible reason could be the difference in rating behavior as observed by the representatives for SME A and SME B. In contrast to the MeSH-based method, the BERT-based search is not affected by these potential biases, since only free text was provided and no MeSH terms are used as input. However, analogous to the MeSH-based search, the BERT-based search could also benefit from incorporating feedback similar to the MeSH-based method to tailor the search more closely to the respective medical device. This could be achieved with the representation of a cluster of seed documents with the respective centroid for the search [51]. Moreover, classification models could leverage BERT embeddings as input and be trained via active-learning feedback loops for increased performance [52; 53]. While both search methods performed overall remarkably well as compared to previous manual as well as automated search methods, the BERT-based method noticeably outperformed the MeSH-based method in the SME use case setting, particularly when searching for clinical trials. One reason for the lower MeSH-based method performance in the first search for publications was that MeSH term relevancies provided by SMEs did not match well with MeSH terms of provided relevant seed documents. This discrepancy may be due to the difference in understanding of MeSH terms by the SMEs and the actual meaning of MeSH terms, resulting in the misidentification of relevant MeSH terms, affecting the success of the search [54]. Feedback sessions also indicated that SMEs may not fully know how to provide a fitting description or labels for their products, putting further emphasis on the importance of additional seed documents for a more precise search. While some MeSH terms were rated as initially irrelevant by the SMEs, publications containing those MeSH terms were rated as relevant. As elaborated then by one SME representative, screening relevant literature helped them to understand better how the medical product could be described and labeled regarding MeSH terms. A second reason for the lower MeSH-based method performance could be the limited screening of only the first top 10 results of the searches since the feedback-driven MeSH-based approach outperformed the BERT-based search on average after screening over 480 documents as indicated in Figure 10.  
A third possible reason for the lower performance when searching for clinical trials is the limited annotation of a clinical trial with at most only a few MeSH terms. While the MTI automatically annotates publications in PubMed with suitable MeSH terms, MeSH terms for clinical trials in ClinicalTrials.gov are added manually or with an algorithm restricted to non-frequently occurring terms describing disease conditions or interventions [55].