Integrating Generative AI Models And Machine Learning Algorithms For Optimizing Clinical Trial Matching And Accessibility In Precision Medicine

Abstract

Novel research endeavors have been extensively conducted in leveraging generative AI models and machine learning algorithms for boosting the clinical trial matching (CTM) in precision medicine. Due to unprecedented collaboration requirements between different data modalities and informational entities, CTM has faced serious challenges that can impair the general implementation of precision medicine plans. Given the emergence of generative AI models in various specialized domains, the deployment of these technologies toward the formation and enrichment and evidenced-based generation of collaborative protocols is particularly examined for CTM. The proposed methodology starts with introducing a comprehensive schematic design of the clinical and pharmacoinformatics generation functions. Additionally, various methodologies relating to the integration of these functions with on-going ML accuracy boosting algorithms to continuously soften the constraints on the accessibility of the clinical and pharmacoinformatics results is delineated. Results are provided following the three-layered architecture featuring discussions on impacts as well as reliability and interpretability concerns. It is demonstrated that despite improved accuracy of the algorithm-driven evidenced generation, generative AI models still substantially enhance the accessibility of the other entities by providing relevant information. Moreover, the translational power of the proposed methodologies across diverse domains is thoroughly discussed to harness proliferation of such postulates to the broad practical realm.