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Ever since its discovery, human immunodeficiency virus type 1 (HIV-1) infection has remained a significant public health concern. The number of HIV-1 seropositive individuals currently stands at 40.1 million, yet definitive treatment for the virus is still unavailable on the market. Vaccination has proven to be a potent tool in combating infectious diseases, as evidenced by its success against other pathogens. However, despite ongoing efforts and research, the unique viral characteristics have prevented the development of an effective anti-HIV-1 vaccine. In this review, we aim to provide an historical overview of the various approaches attempted to create an effective anti-HIV-1 vaccine. Our objective is to explore the reasons why specific methods have failed to induce a protective immune response and to analyze the different modalities of immunogen presentation. This trial is registered with NCT05414786, NCT05471076, NCT04224701, and NCT01937455. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined. [ABSTRACT FROM AUTHOR]