## 3 Results  
We identified the top 50 most cited papers from a full list of nearly 75,000 works within the inspected period. All data relating to the selected papers is available in the Supplementary Materials. Based on the top 50 papers that were returned, we identified five common themes that encompass broad sets of categories and placed each paper into them (Table 1). The first category, Advancements in Protein Structure Prediction, had nine papers that focused on AI for predicting protein structures, an area that has seen significant growth and innovation, particularly in the understanding of protein folding and function [6; 7; 8; 9; 10; 11; 12; 13; 14]. The second category, Real-World Clinical Implementation, had 14 papers and generally explored the practical applications of AI in healthcare, including diagnostics, educational tools, and telemedicine, highlighting how AI technologies are transforming patient care and medical education [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. The third category, Ethical and Regulatory Aspects, had five papers and primarily dealt with the critical examination of AI, focusing on ethical considerations, transparency, and the explainability of clinical AI systems [29; 30; 31; 32; 33]. The next category, Tools for Disease Subtyping and Drug Development in Personalized Medicine, had 15 papers and focused on leveraging AI tools for reclassification of disease through subtyping and patient stratification as well as how drugs can be developed towards this personalization of medicine [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. The last category, Molecular and Multi-Omic Biology, had seven papers and showcased the use of AI on genomic, transcriptomic, and proteomic data for understanding complex molecular biology processes [49; 50; 51; 52; 53; 54; 55].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Categories** & **Number of Papers** & **Notable Journals** & **Notable Paper (PMID)** & **Data Types** & **Description** \\ \hline Protein Structure Prediction & 9 & Nature, Science & 34265844 & Structural Data, Protein Sequences & Advances in predicting protein structures using AI are significantly impacting biological research \\ \hline Real-World Clinical Implementation & 14 & PLOS: Digital Health, Nature Medicine & 33629156 & Imaging Data, Clinical Data & Development and deployment of AI for diagnostic accuracy and efficiency in clinical settings \\ \hline Ethical and Regulatory Aspects & 5 & The Lancet: Digital Health, Nature Medicine & 34711379 & Policy and Regulation Documents, Clinical Data & Highlighting the need for ethical guidelines, regulatory standards, and possibly explainability in AI applications in healthcare \\ \hline Tools for Disease Subtyping and Classification & 15 & Nucleic Acids Research, Nature Methods & 33649564 & Imaging Data, Genomic Data & Tools and methods for how AI can be used to refine disease classification, subtyping, and drug discovery \\ \hline Molecular and Multi-Omic Biology & 7 & Nature Genetics, Bioinformatics & 33876751 & Clinical Data, Multi-Omic Data & Advanced AI applications for understanding complex multi-omic and molecular interactions \\ \hline \hline \end{tabular}  
A summary table outlining common classification categories for the papers represented in this work. This table reflects examples of journals, notable papers, data types, and a description of each highlighted category.  
\end{table}
Table 1: Categorical Overview of Trends by Classification Category.