[论文解读] 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 AI被引用 11

一句话总结

该论文提出一个以AI驱动的多学科框架，通过整合基因组学、蛋白质组学、微生物组学和临床数据来支持诊断、治疗和预后，从而推进传染性疾病及其他复杂疾病的精准医学。

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.

研究动机与目标

• 将精准医学定位为以患者为中心的方法，应用于传染性疾病与复杂疾病。
• 提出一个整合AI框架，将多组学与临床数据结合起来，以为诊断、治疗和预后提供信息。
• 突出跨学科协作在完善模型、解决伦理与鲁棒性等考虑中的作用。
• 提出AI驱动方法在感染之外的疾病中的适用性，并识别隐私、法规和临床工作流整合方面的空白。

提出的方法

• 使用AI算法分析整合的患者档案，包括基因组学、蛋白质组学、微生物组学和临床数据。
• 开发预测模型以支持精准诊断、治疗计划制定和预后评估。
• 强调来自不同领域的专家之间的迭代协作，以完善AI模型并确保伦理、鲁棒的应用。

实验结果

研究问题

• RQ1 哪些AI驱动的策略可以整合多组学与临床数据，以提升传染性疾病的精准医学？
• RQ2 跨学科协作如何提升精准医学中AI的鲁棒性与伦理部署？
• RQ3 整合方法如何扩展到感染之外的其他复杂疾病？

主要发现

• AI驱动的预测建模可以为传染性疾病的个性化干预提供依据。
• 提出通过多学科协作来完善AI模型并解决伦理与鲁棒性方面的考量。
• 该方法在感染之外的其他复杂疾病中具有潜在适用性。