AI

Congenital split hand/split foot malformation (SHFM) is a complex congenital disorder with a highly variable clinical presentation and a poorly understood molecular basis, particularly associated with chromosome 10q24 microduplications. This review examines the genetic and molecular mechanisms underlying SHFM, with a focus on the role of microduplications in altering key genes such as FBXW4 and FGF8. It explores the integration of artificial intelligence (AI) in genomic data analysis, protein structure prediction, and protein-protein interaction modeling to enhance our understanding of the condition. Furthermore, the application of AI in optimizing smart biomaterials for personalized treatment strategies and gene editing technologies is discussed, offering novel approaches for regenerative repair. The review highlights how AI and advanced genomic tools can together provide new insights into SHFM's pathology, with potential for transformative clinical applications in diagnosis and treatment.

The rapid advancement of artificial intelligence (AI) and robotics is progressively transforming traditional medical practices. This review highlights the latest developments in the application of AI and robotics in the medical field, exploring their roles in diagnosis, treatment, surgery, nursing, and health management, while also addressing the challenges and future potential they present. By analyzing relevant research findings, this paper aims to provide healthcare professionals and researchers with a comprehensive perspective to better understand this swiftly evolving domain.

With the continuous advancement in tumor research, universal models have emerged as a vital research tool, increasingly recognized for their role in understanding tumor biology, evaluating treatment efficacy, and predicting disease prognosis. This review outlines the applications of universal models in tumor research, highlighting their significance in elucidating tumorigenesis mechanisms, drug development, personalized therapy, and clinical trials. Furthermore, it addresses the current challenges faced by universal models and explores future development directions, offering researchers new perspectives and insights.

The rapid advancement of artificial intelligence (AI) technologies, particularly the emergence of large models, has ushered in unprecedented opportunities within the medical field. The application of open-source large models enables more efficient utilization of data in medical research and clinical practice, enhancing the precision of diagnosis and treatment. This review outlines the current status of AI large models in medicine, exploring their potential in disease prediction, image analysis, and personalized treatment. Furthermore, it analyzes the challenges faced by these technologies and discusses future development directions, emphasizing the need for improved methodologies and collaborative efforts to maximize their impact on healthcare.

AlphaFold is a deep learning-based protein structure prediction tool with immense potential in biomedical research. This paper aims to explore the multifaceted applications of AlphaFold in the medical field, including its roles in protein structure prediction, elucidation of disease mechanisms, and drug discovery. By analyzing current research advancements, this review will demonstrate how AlphaFold is accelerating the drug development process and discuss its prospects in personalized and precision medicine. The integration of AlphaFold's capabilities into various aspects of medical research not only enhances our understanding of protein functions but also paves the way for innovative therapeutic strategies.