Advancing Biomedicine Through Computing, Networks, and AI: Opportunities and Challenges

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d82043788e88">The rapid transformation of biomedicine is being propelled by the powerful convergence of high-performance computing (HPC), large-scale data integration, network-based approaches, and artificial intelligence (AI). This talk will examine how these synergistic technologies are revolutionizing biomedical research, while critically addressing their current limitations and future challenges. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d82043788e90">HPC has emerged as an essential tool for processing and interpreting massive biomedical datasets, driving breakthroughs in disease mechanism discovery and accelerating the identification of novel therapies. However, realizing its full potential requires overcoming persistent barriers in data accessibility, standardization, and interoperability. Complementing these computational advances, network science provides a robust framework for deciphering disease complexity—from large-scale comorbidity networks derived from electronic health records to molecular-level interactomes that reveal shared genetic and pathway-level underpinnings of disease. These network approaches are opening new avenues for understanding disease etiology, advancing precision medicine, and uncovering promising opportunities for drug repurposing. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d82043788e92">Simultaneously, the explosive growth of generative AI and autonomous AI agents presents both unprecedented opportunities and critical challenges for biomedicine. Key questions arise about how to effectively integrate these AI systems with established network-based and mechanistic modeling approaches, while developing rigorous methodologies to mitigate inherent limitations such as bias, interpretability gaps, and reproducibility concerns. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d82043788e94">In this talk, I will highlight transformative advances at the intersection of HPC, network medicine, and AI, with a strong emphasis on validation frameworks, responsible innovation, and multidisciplinary collaboration to ensure these computational breakthroughs translate into tangible scientific and clinical benefits.

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Publication Details

Published
2025-07-14
DOI
https://doi.org/10.58647/rexpo.25000109.v1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Advancing Biomedicine Through Computing, Networks, and AI: Opportunities and Challenges

Alfonso Valencia
Artificial Intelligence in Healthcare and Education
article

Advancing Biomedicine Through Computing, Networks, and AI: Opportunities and Challenges

Alfonso Valencia
article en

Abstract

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d82043788e88">The rapid transformation of biomedicine is being propelled by the powerful convergence of high-performance computing (HPC), large-scale data integration, network-based approaches, and artificial intelligence (AI). This talk will examine how these synergistic technologies are revolutionizing biomedical research, while critically addressing their current limitations and future challenges. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d82043788e90">HPC has emerged as an essential tool for processing and interpreting massive biomedical datasets, driving breakthroughs in disease mechanism discovery and accelerating the identification of novel therapies. However, realizing its full potential requires overcoming persistent barriers in data accessibility, standardization, and interoperability. Complementing these computational advances, network science provides a robust framework for deciphering disease complexity—from large-scale comorbidity networks derived from electronic health records to molecular-level interactomes that reveal shared genetic and pathway-level underpinnings of disease. These network approaches are opening new avenues for understanding disease etiology, advancing precision medicine, and uncovering promising opportunities for drug repurposing. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d82043788e92">Simultaneously, the explosive growth of generative AI and autonomous AI agents presents both unprecedented opportunities and critical challenges for biomedicine. Key questions arise about how to effectively integrate these AI systems with established network-based and mechanistic modeling approaches, while developing rigorous methodologies to mitigate inherent limitations such as bias, interpretability gaps, and reproducibility concerns. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d82043788e94">In this talk, I will highlight transformative advances at the intersection of HPC, network medicine, and AI, with a strong emphasis on validation frameworks, responsible innovation, and multidisciplinary collaboration to ensure these computational breakthroughs translate into tangible scientific and clinical benefits.

Barcelona Supercomputing Center (ES), CNS Research (United States) (US), Universitat Politècnica de Catalunya (ES)
Openalex Percentile: Top 88%
Artificial Intelligence in Healthcare and Education
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