The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with NAFLD phenotypes. However, there are fewer studies and replicability in lesser-known genes such as LYPLAL1 and glucokinase regulator (GCKR). With the advent of artificial intelligence (AI) in clinical genetics, studies have utilized AI algorithms to identify phenotypes through electronic health records and utilize convolution neural networks to improve the accuracy of variant identification, predict the deleterious effects of variants, and conduct phenotype-to-genotype mapping. Natural language processing (NLP) and machine-learning (ML) algorithms are popular tools in GWAS studies and connect electronic health record phenotypes to genetic diagnoses using a combination of international classification disease (ICD)-based approaches. However, there are still limitations to machine-learning - and NLP-based models, such as the lack of replicability in larger cohorts and underpowered sample sizes, which prevent the accurate prediction of genetic variants that may increase the risk of NAFLD and its progression to advanced-stage liver fibrosis. This may be largely due to the lack of understanding of the clinical consequence in the majority of pathogenic variants. Though the concept of evolution-based AI models and evolutionary algorithms is relatively new, combining current international classification disease -based NLP models with phylogenetic and evolutionary data can improve prediction accuracy and create valuable connections between variants and their pathogenicity in NAFLD. Such developments can improve risk stratification within clinical genetics and research while overcoming limitations in GWAS studies that prevent community-wide interpretations.
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.