[Paper Review] Integrative AI-Driven Strategies for Advancing Precision Medicine in Infectious Diseases and Beyond: A Novel Multidisciplinary Approach

Ghizal fatima, Risala H. Allami+1 more|arXiv (Cornell University)|Jul 27, 2023

COVID-19 diagnosis using AI11 citations

TL;DR

The paper proposes an AI-driven, multidisciplinary framework to advance precision medicine in infectious diseases and other complex conditions by integrating genomics, proteomics, microbiomics, and clinical data to support diagnosis, treatment, and prognosis.

ABSTRACT

Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious diseases and beyond. It integrates diverse fields, analyzing patients' profiles using genomics, proteomics, microbiomics, and clinical data. AI algorithms process vast data, providing insights for precise diagnosis, treatment, and prognosis. AI-driven predictive modeling empowers healthcare providers to make personalized and effective interventions. Collaboration among experts from different domains refines AI models and ensures ethical and robust applications. Beyond infections, this AI-driven approach can benefit other complex diseases. Precision medicine powered by AI has the potential to transform healthcare into a proactive, patient-centric model. Research is needed to address privacy, regulations, and AI integration into clinical workflows. Collaboration among researchers, healthcare institutions, and policymakers is crucial in harnessing AI-driven strategies for advancing precision medicine and improving patient outcomes.

Motivation & Objective

• Motivate precision medicine as a patient-centric approach for infectious diseases and complex conditions.
• Propose an integrative AI framework that combines multi-omics with clinical data to inform diagnosis, treatment, and prognosis.
• Highlight the role of cross-disciplinary collaboration to refine models and address ethical and robustness considerations.
• Suggest applicability of the AI-driven approach to diseases beyond infections and identify gaps in privacy, regulations, and clinical workflow integration.

Proposed method

• Use AI algorithms to analyze integrated patient profiles including genomics, proteomics, microbiomics, and clinical data.
• Develop predictive models to support precise diagnosis, treatment planning, and prognosis assessment.
• Emphasize iterative collaboration among experts from diverse domains to refine AI models and ensure ethical, robust applications.

Experimental results

Research questions

• RQ1 What AI-driven strategies can integrate multi-omics and clinical data to improve precision medicine for infectious diseases?
• RQ2 How can collaboration across disciplines enhance the robustness and ethical deployment of AI in precision medicine?
• RQ3 In what ways can the integrated approach be extended to other complex diseases beyond infections?

Key findings

• AI-enabled predictive modeling can inform personalized interventions for infectious diseases.
• A multidisciplinary collaboration is proposed to refine AI models and address ethical and robustness considerations.
• The approach has potential applicability beyond infections to other complex diseases.