## 4 Results  
### Research Topics: Semantic Space for mHTI Scholar Publications  
Following the pipeline described in Section 3.2, a total of 12,838 of the 16,580 articles are clustered into 329 topics, leaving 3742 articles as noise in the semantic latent space. Figure 4 presents a 2D UMAP visualization of the high-dimensional embedding vectors, color-coded according to the 329 topic labels generated by our topic modeling process. Each dot represents a publication of the mHTI scholars. The color is coded by research topic, while unlabeled topics (classified as noise by the HDBSCAN algorithm) are coded in light gray. The distribution of the topic size is presented in Figure 5. Only 4.0% of the topics include more than 100 articles, while 45.6% of the topics include fewer than 25 articles. The distribution of the topic size indicates that mHealth scholars works on diverse research topics, while many such topics are studied by a relatively small number of scholars, meaning they are new or at its developing phase. For example, one cluster with 11 articles primarily focuses on the use of AI in healthcare, with all published after 2020. To further illustrate, we list the top five topics with the largest cluster sizes in Table 2 and five topics with smallest cluster sizes in Table 3, along with their GPT-generated descriptions. Empirical manual inspection confirms that the assigned documents align well with their respective topic labels. We provide a complete table of 329 topics in the Supplementary Materials. Figure 4: Semantic space for the mHTI scholars’ publications. Different colors indicate 329 topic labels. Unlabeled topics are coded in light gray. **Remark 1**. : _We observed that during the process of topic and description generation, GPT sometimes produced topic descriptions that include specific geographic names, such as disease research in a particular country, which is not shared by all papers in the cluster and is prone to change when another sample is selected. Therefore, we designed an additional prompt to refine the generated labels, removing specific geographic information from the topic descriptions. The prompt is also provided in the Appendix C._  
Due to the complexity of interpreting 329 micro-topics, we further cluster the topics at a higher level to facilitate the interpretation of the semantic space. With the combination of HDBSCAN and GPT-40 as well as manual human validation, 14 general topics are identified. We use GPT's contextual understanding to directly assign general topics to the 329 micro-topics, rather than relying on clustering algorithms. For this approach, it is crucial that the GPT-assigned general topic labels align with the semantic space. Specifically, micro-topics belonging to the same general topic should be located in close proximity within the semantic space. The final result, visualized in Figure 6, demonstrates strong alignment between the location of semantic spaces and the assigned general topics, validating the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Topic** & **Description** & **Size** \\ \hline \multirow{2}{*}{Eating Disorders Research} & Investigation of eating disorder symptoms, interventions, and impacts across various populations. & 202 \\ \hline \multirow{2}{*}{Pediatric Asthma Management} & Focus on improving techniques, adherence, and care outcomes in children with asthma. & 198 \\ \hline \multirow{2}{*}{Virtual Reality Assessment} & Evaluating cognitive functions using VR technology improves accuracy and ecological validity. & 191 \\ \hline \multirow{2}{*}{Alcohol Use in HIV} & Exploring impacts and patterns of alcohol use among HIV patients. & 159 \\ \hline \multirow{2}{*}{Behavioral Weight Loss} & Focuses on interventions and strategies to promote weight loss in diverse populations. & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top 5 largest topics, along with their descriptions and sizes. Figure 5: Distribution of the sizes (number of publications) of the 329 topics. coherence of our topic modeling approach.