## 5 Discussion  
The primary objective of this research paper was to tackle the challenges faced by medical practitioners when seeking timely and comprehensible search results within the healthcare domain. We specifically delved into the realms of ease of reading and technicality in medical research articles, with the ultimate aim of enhancing IR for medical professionals, thereby augmenting their decision making capabilities and improving patient care outcomes.  
The dataset compiled for this study consists of 209 scientific research abstracts from diverse medical disciplines, available in both English and German. Each abstract underwent annotation by medical practitioners, who assigned ease of reading and technicality scores. The dataset analysis provided valuable insights into the linguistic characteristics of medical abstracts and revealed differences in technicality and ease of reading between English and German abstracts. Notably, our findings revealed that German abstracts tended to be more technically challenging, while English abstracts were slightly easier to read.  
Our study introduced two regression models, namely BERT-Readability and BERT-Technicality, which proved highly effective in predicting ease of reading and technicality scores for the abstracts. These models outperformed existing readability formulas commonly employed in the literature, underscoring their significance in predicting domain-specific readability aspects. This highlights the critical importance of employing domain-specific models tailored to scientific research abstracts for precise readability assessment.  
The integration of ease of reading and technicality aspects into search engines has practical implications for medical practitioners. Personalized search results, based on language proficiency and expertise, empower medical professionals with efficient and relevant IR tools. This customization enhances the relevance and accessibility of information, optimizing decision making and patient care. Our research paper underscores the significance of tailoring readability assessment to the specific needs of medical practitioners, leading to improved information utilization and overall usability of search engines in the medical domain.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Formula** & **English** & **German** \\ \hline BERT-Technicality & **9.42** & **10.07** \\ BERT-Readability & **10.61** & **11.80** \\ \hline Coleman-Liau Index & 19.96 & 20.22 \\ SMOG Index & 21.28 & 23.59 \\ Gunning Fog Index & 26.13 & 30.66 \\ Dale-Chall Readability Score & 26.77 & 23.61 \\ Flesch-Kincaid Grade Level & 27.93 & 28.38 \\ Automated Readability Index & 30.95 & 30.26 \\ Gutierrez de Polini Index & 33.90 & 31.98 \\ Szigriszt-Pazos Index & 32.93 & 30.94 \\ Fernandez-Huerta Index & 32.30 & 31.17 \\ Flesch Reading Ease & 34.34 & 32.60 \\ Gulpease Index & 34.46 & 35.48 \\ \hline \hline \end{tabular} All formulas are available in Textstat library. [[https://github.com/textstat/textstat](https://github.com/textstat/textstat)]([https://github.com/textstat/textstat](https://github.com/textstat/textstat)), accessed on 1 August 2023.  
\end{table}
Table 2: Formulas and our models’ performance on the dataset: RMSE scores comparison.