AI-empowered visualization of nucleic acid testing

Aims: The visualization of nucleic acid testing (NAT) results plays a critical role in diagnosing and monitoring infectious and genetic diseases. The review aims to review the current status of AI-based NAT result visualization. It systematically introduces commonly used AI-based methods and techniques for NAT, emphasizing the importance of result visualization for accessible, clear, and rapid interpretation. This highlights the importance of developing a NAT visualization platform that is user-friendly and efficient, setting a clear direction for future advancements in making nucleic acid testing more accessible and effective for everyday applications. Method: This review explores both the commonly used NAT methods and AI-based techniques for NAT result visualization. The focus then shifts to AI-based methodologies, such as color detection and result interpretation through AI algorithms. The article presents the advantages and disadvantages of these techniques, while also comparing the performance of various NAT platforms in different experimental contexts. Furthermore, it explores the role of AI in enhancing the accuracy, speed, and user accessibility of NAT results, highlighting visualization technologies adapted from other fields of experimentation. Significance: This review offers valuable insights for researchers and everyday users, aiming to develop effective visualization platforms for NAT, ultimately enhancing disease diagnosis and monitoring.