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AI integration with multiomics: Transformative leap in healthcare, says GlobalData

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AI integration with multiomics

In the rapidly evolving field of healthcare, artificial intelligence (AI) in multiomics, an approach that draws together data from multiple “omic” techniques such as genomics, epigenomics, transcriptomics, and proteomics, is marking a significant impact. This innovative merger harnesses AI’s computational strength alongside the rich, varied insights of multiomics. It is revolutionizing the fields of personalized medicine and disease analysis, marking a significant advancement in medical strategies and future health interventions, finds GlobalData, a leading data and analytics company.

Rahul Kumar Singh, Senior Analyst of Disruptive Tech at GlobalData, comments: “The convergence of AI and multiomics is enabling to delve deeper into the complexities of human biology. Through AI’s advanced analytics, we can interpret vast multiomic data, leading to breakthroughs in the early disease detection, personalized therapy, and efficient drug development. This transformative approach is a leap forward, moving from a one-size-fits-all model to highly individualized patient care, shaping the future of medicine.”

An analysis by GlobalData’s Disruptor Intelligence Center reveals notable progress in AI applications within multiomics, marking a shift in healthcare research and treatment approaches. The collaboration between Google Cloud’s Multiomics suite and Quantiphi highlights this trend, focusing on improving the analysis of genomic data for drug discovery and precision medicine. This partnership reflects a growing integration of AI in interpreting complex biological data.

Similarly, Illumina and AstraZeneca’s partnership applies AI for genome analysis to speed up the discovery of drug targets. Their work is significant in enhancing the effectiveness and efficiency of drug development processes, utilizing extensive multi-omics data.

The AI Collaborative, launched by Nuance (a Microsoft company) and The Academy, also aligns with this direction. It aims to apply AI in areas like precision medicine and clinical decision-making, addressing the challenges in healthcare such as clinician workload and patient care.

Deep 6 AI’s introduction of an AI-powered genomics module for oncology clinical trials exemplifies the practical application of AI in healthcare. It uses natural language processing to match patients to trials more quickly and accurately, demonstrating how AI can streamline research processes.

ConcertAI’s TeraRecon brings AI into clinical trials and patient care, focusing on managing and developing AI models. This initiative underscores the role of AI in enhancing research methodologies and clinical decision-making, further integrating AI into the fabric of healthcare innovation.

Singh concludes: “This fusion is not just a technological upgrade; it is a paradigm shift in healthcare research and practice. The potential is immense, from unraveling the intricate mechanisms of diseases to tailoring bespoke treatment protocols. However, the path forward requires navigating challenges like ethical data usage, ensuring continued funding, and fostering interdisciplinary collaboration. Overcoming these hurdles is essential to fully harness AI’s power in multiomics, driving innovation and excellence in patient care and pharmaceutical development.”

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