AI and Precision Medicine: Innovations and Applications

In the evolving landscape of healthcare, personalized medicine stands at the forefront of promising transformative changes in disease prevention, diagnosis, and treatment. The intricate understanding of individual variability in genes, environment, and lifestyle for each person paves the way for tailor-made therapeutic strategies. Central to realizing this potential is the integration of diverse data modalities, including genomics, transcriptomics, metabolomics, proteomics, molecular interactions, clinical observations, laboratory results, medical imaging, and digital health device outputs. This call for papers invites groundbreaking research that leverages Artificial Intelligence (AI) and Machine Learning (ML) technologies to harness these vast and varied data sources, driving innovations in personalized medicine. 

We seek original contributions that present new AI methods (ML, CNN, Deep Learning, Generative AI, Transformers, Diffusion AI, etc) or novel applications of existing methodologies within translational informatics and clinical research informatics domains. Papers should focus on the development and application of AI techniques to integrate and analyze multi-omic data, clinical metrics, and digital health indicators, aiming to enhance patient-specific diagnosis, treatment planning, and health outcome prediction. In addition, we are particularly interested in nonlinear variable interactions: synergistic and antagonistic, leading to predictions that would not have been straightforward by conventional analytics. Submissions may cover, but are not limited to, AI models for analyzing complex biological data, ML algorithms for identifying disease biomarkers, AI-driven tools for clinical decision support, and innovative applications of AI in monitoring and managing patient health through digital devices. Data may come from 'omics technologies, medical imaging (MRI, fMRI, CT-SCANs, X-rays, Pathology Imaging, Videos of patient-clinicians encounters, etc), digital health, telehealth, personal devices (iwatch, phones, wearable ECGs, heart rate monitors, pedometers, etc), laboratory systems, electronic health records, clinical data warehouses, EEGs, ICU monitoring, a combination of these data modalities, etc.