PRISM: Ethically-guided multimodal AI models for predicting disease pathogenesis in individuals with pathogenic variants

About This Grant

PROJECT SUMMARY This project aims to develop machine learning and artificial intelligence tools to enhance prediction of disease manifestation in individuals with pathogenic genetic variants. We will focus on Alzheimer's disease and cardiomyopathy, leveraging diverse data from large biobanks and healthcare systems to create a more comprehensive and nuanced understanding of how genetic variants interact with other factors to influence disease onset and progression. Our interdisciplinary team will create an ethical framework to guide tool development and implementation, integrating genomic, clinical, imaging, and other data types into a common model. We will build and validate tools to predict disease onset, progression, and treatment response. We will then perform rigorous cross-validation to assess the generalizability of these tools across different datasets and clinical settings, ensuring their robustness and applicability in diverse healthcare contexts. Using a combination of advanced AI and interpretable machine learning methods, we aim to identify novel biomarkers for disease that could be more readily translated into clinical practice. Integrated with the technical development, we will conduct research on the ethical, legal, and social implications (ELSI) of incorporating these tools into genomic medicine. The results of these ELSI research projects will be further integrated into our ethical framework to inform the development of the optimal AI tools. This project will advance the field by improving risk stratification for variant carriers and identifying factors influencing disease beyond genetics alone. We aim to disseminate validated tools, best practices, and lessons learned to the broader research community, potentially transforming how genetic risk is assessed and managed in clinical practice. By combining cutting-edge computational methods with careful ethical consideration, we hope to create resources that can significantly enhance patient care and our understanding of complex genetic diseases. Ultimately, this work will lead to more targeted prevention strategies, personalized treatment plans, and improved outcomes for individuals at risk of Alzheimer's disease, cardiomyopathy, and other genetically influenced conditions.