## 4 Discussion
### Highlighted Impressions and Overall Themes
#### 4.1.4 Tools for Disease Subtyping and Drug Development in Personalized Medicine  
An emerging trend in personalized medicine is the exploration of the fact that complex diseases are multi-faceted and heterogeneous in nature. It is becoming increasingly clear that disease etiologies, manifestations, and outcomes have patterns at the molecular and phenotypic level that can be differentiated between groups of patients. For true precision medicine to exist, the challenge of accurately diagnosing and treating complex diseases hinges significantly on the ability to effectively subtype these conditions. Disease subtyping is when one heterogenous disease can be stratified into subgroups that have differentiated patterns, leading to targeted and personalized treatment plans. This facet is particularly vital in cancer treatment, where the effectiveness of therapies can vary greatly depending on the specific type and stage of cancer. The outcome of using tools to address these goals can help drug development itself. One review paper describes how AI can be applied across the lifecycle of pharmaceutical products, from drug discovery to clinical trial design [35]. On the development side, the review article further describes AI applications in drug repurposing, screening, and chemical synthesis. The paper also provides a useful table for AI tools that have been developed for processing different types of data in silico. In terms of disease applications, one study developed a deep learning methodology in computational pathology to efficiently analyze whole-slide pathology images, which are time-consuming to annotate and categorize [39]. The authors developed clustering-constrained-attention multiple-instance learning (CLAM) to identify subregions of high diagnostic value, to accurately classify whole slides, and for instance-level clustering. They applied CLAM to three tasks, specifically the subtyping of renal cell carcinoma and non-small-cell lung cancer, as well as the detection of lymph node metastasis, and showed that it was able to localize well-known morphological features. In addition to the subtyping of known cancers, like in the prior work, another paper created a deep-learning-based algorithm, specifically Tumor Origin Assessment via Deep Learning (TOAD), to classify cancers of unknown primary origin. Identifying the cancer's source is critical for determining the most effective treatment strategy [43]. This technique had strong performance, achieving a top-1 accuracy of 0.83 on an internal, held-out set and 0.80 on an external testing set.  
In addition to analyzing images for disease classification and subtyping, analyzing the connection between genotypes and phenotypes is pivotal in personalized medicine as it can reveal underlying genetic and biological features that differentiate etiologies and outcomes. As such, with the increased availability of larger and richer patient datasets, performing this analysis has become computationally expensive, especially when accounting for relatedness within the population structure. As such, one group has developed REGENIE, which is designed to fit a whole-genome regression model for quantitative and binary traits in a computationally efficient manner while maintaining statistical efficiency [45]. By accommodating multiple phenotypes and requiring only local segments of the genotype matrix, REGENIE substantially reduces compute time and memory usage, which the authors demonstrate in large-scale studies utilizing the UK Biobank. This work exemplifies AI approaches that create solutions for effective analyses of personalized medicine-related datasets, which are growing at substantial rates and for which traditional techniques are infeasible. Collectively, these studies underscore the pivotal role of AI methods in overcoming the challenges of disease subtyping, enhancing diagnostic accuracy, and paving the way for large-scale genotype/phenotype analyses to discover patterns that produce treatments targeted towards individual patient genetic profiles.