Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities.

IF 2.9 4区 医学 Q3 IMMUNOLOGY Archivum Immunologiae et Therapiae Experimentalis Pub Date : 2024-02-29 eCollection Date: 2024-01-01 DOI:10.2478/aite-2024-0006
Bhagirath Singh, Anthony M Jevnikar, Eric Desjardins
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Abstract

The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.

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人工智能、大数据和免疫监管:挑战与机遇。
免疫系统受一系列复杂的基因、分子和细胞相互作用的调节。一系列 "全息"(omics)技术产生了海量数据,达到了大数据(BD)的地位,推动了免疫及其相互作用网络研究的快速发展。随着人工智能(AI)的最新发展,理论和临床研究可能会出现突破。利用人工智能工具对大型数据集进行分析,可以提出新的可检验假设,开辟新的研究途径,并为调节免疫和治疗免疫疾病提供创新战略。这包括诊断和识别罕见疾病,预防和治疗自身免疫性疾病、过敏性疾病、传染性疾病、代谢组疾病、癌症和器官移植。然而,如何利用这些研究来促进我们对基础免疫学的了解,以及如何在健康和疾病中调节免疫,在伦理和监管方面仍然存在挑战。这对于同时发生复杂的相互作用和多种细胞通路的实体尤为重要,因为传统方法无法理解和治疗这些实体。人工智能对生物多样性的分析可能会很复杂,因为调节免疫力的积极和消极结果都可能会产生重要的伦理影响,需要加以考虑。我们建议,当务之急是制定指导方针,说明人工智能工具对免疫学 BD 的分析应如何指导以免疫为基础的干预措施,以便在伦理框架内治疗各种疾病、预防感染和保持健康。
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来源期刊
CiteScore
5.90
自引率
0.00%
发文量
26
审稿时长
>12 weeks
期刊介绍: Archivum Immunologiae et Therapiae Experimentalis (AITE), founded in 1953 by Ludwik Hirszfeld, is a bimonthly, multidisciplinary journal. It publishes reviews and full original papers dealing with immunology, experimental therapy, immunogenetics, transplantation, microbiology, immunochemistry and ethics in science.
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