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AI and Precision Medicine: Innovations and Applications

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.


The objective is to showcase research that contributes to personalizing healthcare and demonstrates how AI and ML can bridge the gap between vast data sources and clinical applications. Successful submissions will highlight novel approaches to data integration, analysis, and interpretation, providing insights that significantly advance the field of personalized medicine. 

Contributions are encouraged from a broad range of disciplines, including translational bioinformatics, clinical research informatics, computational biology, clinical informatics, data science, and digital health. Papers should emphasize the methodological innovations, the depth of data integration, and the potential impact on healthcare personalization. By highlighting the role of AI in unlocking the power of multi-omic and clinical data, this call aims to foster the development of technologies and strategies that bring personalized medicine closer to reality, ensuring more precise, predictive, and preventive healthcare solutions.


  • Integrative analysis of multi-omic data using AI
  • AI-driven wearable and digital health device data analysis
  • AI-enhanced analysis of of patient-clinician encounter videos
  • AI in genomic variant interpretation
  • AI applied to telehealth and remote patient monitoring
  • Predictive analytics using EHR, imaging and omics data
  • AI models for predictive biomarker identification
  • Data fusion and interoperability augmenting AI applications to personalized medicine
  • Wearable device data and AI in chronic disease management
  • AI approaches to personalized drug repositioning or rescue
  • Ethical, legal and social implications of AI in personalized medicine
  • AI in ICU
  • AI-driven personalized therapies from multiple modalities of data
  • AI in predicting drug response and adverse reaction or super-responder
  • Ai-driven fusion of pathology imaging and genetic data for cancer diagnosis
  • Clinical Decision Support systems powered by AI
  • Transformative AI technologies in mental health care
  • Personalized medication management through AI analysis of EHR and pharmacy data
  • AI for modeling of complex disease or rare non-medalian disorders
  • AI and nonlinear variable interactions in disease prediction and management
  • Digital biomarkers from wearables and mobile devices for disease prediction
  • AI applications in medical imaging for precision medicine
  • Multimodal data integration for comprehensive health profiles and digital twins






5 minutes

Podium presentation 

Welcome address

Yves Lussier

40 Minutes

Podium presentation

Opening Keynote

Zhongming Zhao

60 minutes

Research presentation

Various research topics on using novel AI/ML methods

Communication author of 3 to 4 accepted papers

15 minutes


Connect and collaborate


60 minutes

Research presentation

Various research topics on using novel AI/ML methods

Communication author of 3 to 4 accepted papers

30 minutes

Interactive panel

Q&A on research topics


20 minutes

Podium talk

Closing Keynote

Mattia Prosperi


John Thomas Menchaca, MD
Resident, Medicine Emory University
Nima Pouladi, MD, PhD
Research Associate, Biomedical Informatics The University of Utah
Mahdieh Shabanian
AI Data Scientist, Biomedical Informatics The University of Utah
Yves A. Lussier, MD
Professor and Chair, Biomedical Informatics The University of Utah
Mattia Prosperi, PhD
Professor And Associate Dean For AI And Innovation The University of Florida
Zhongming Zhao, PhD, MS
Chair Professor for Precision Health; Director, Center for Precision Health University of Texas in Houston
Katie (Xinxin) Zhu, MD, PhD, MSc
Executive Director, Center for Biomedical Data Science Yale University