Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, and immunology data. This comprehensive review explores the clinical applications of AI-driven analytics in unlocking personalized insights for patients with autoimmune rheumatic diseases. Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. AI-enabled immunology data analysis, including dimensionality reduction, cell population identification, and sample classification, provides unprecedented insights into complex immune responses. The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. The future of precision medicine lies in the continued research, development, and clinical integration of AI-driven strategies to unlock personalized patient care and drive innovation in rheumatology.

Background: Multi-genome comparative analysis has yielded important insights into the molecular details of gene regulation. We have developed EvoPrinter, a web-accessed genomics tool that provides a single uninterrupted view of conserved sequences as they appear in a species of interest. An EvoPrint reveals with near base-pair resolution those sequences that are essential for gene function. Results: We describe here EvoPrinterHD, a 2^nd-generation comparative genomics tool that automatically generates from a single input sequence an enhanced view of sequence conservation between evolutionarily distant species. Currently available for 5 nematode, 3 mosquito, 12 Drosophila, 20 vertebrate, 17 Staphylococcus and 20 enteric bacteria genomes, EvoPrinterHD employs a modified BLAT algorithm [enhanced-BLAT (eBLAT)], which detects up to 75% more conserved bases than identified by the BLAT alignments used in the earlier EvoPrinter program. The new program also identifies conserved sequences within rearranged DNA, highlights repetitive DNA, and detects sequencing gaps. EvoPrinterHD currently holds over 112 billion bp of indexed genomes in memory and has the flexibility of selecting a subset of genomes for analysis. An EvoDifferences profile is also generated to portray conserved sequences that are uniquely lost in any one of the orthologs. Finally, EvoPrinterHD incorporates options that allow for (1) re-initiation of the analysis using a different genome's aligning region as the reference DNA to detect species-specific changes in less-conserved regions, (2) rapid extraction and curation of conserved sequences, and (3) for bacteria, identifies unique or uniquely shared sequences present in subsets of genomes. Conclusion: EvoPrinterHD is a fast, high-resolution comparative genomics tool that automatically generates an uninterrupted species-centric view of sequence conservation and enables the discovery of conserved sequences within rearranged DNA. When combined with cis-Decoder, a program that discovers sequence elements shared among tissue specific enhancers, EvoPrinterHD facilitates the analysis of conserved sequences that are essential for coordinate gene regulation.

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.