欧洲克罗恩病和结肠炎组织(ECCO)第九届科学研讨会结果:医疗管理和精准医疗中的人工智能。

IF 8.7
Uri Kopylov, Bram Verstockt, Urko M Marigorta, Daniele Noviello, Peter Bossuyt, Aart Mookhoek, Pieter Sinonque, Alaa El-Hussuna, Kapil Sahnan, Daniel C Baumgart, Nurulamin M Noor, Mariangela Allocca, Dan Carter, Arzu Ensari, Marietta Iacucci, Gianluca Pellino, Alessandra Soriano, Jan de Laffolie, Marco Daperno, Tim Raine, Isabelle Cleynen, Shaji Sebastian
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引用次数: 0

摘要

背景与目的:人工智能(AI)越来越多地应用于包括炎症性肠病(IBD)在内的各个医学领域。作为ECCO第九届IBD人工智能科学研讨会的一部分,本系统综述探讨了人工智能在多组精准医学、文本任务的大语言模型(llm)以及可穿戴和远程护理技术中的应用。方法:对文献进行全面系统的分析,重点关注三个主题:IBD的多组学预测模型;自然语言处理(NLP)和法学硕士用于临床实践、研究和患者交流;以及远程监控和可穿戴设备的作用。结果:有希望的关键领域包括实施NLP和llm,用于病例识别和区分,跟踪疾病活动,药物警戒,质量保证和患者支持。多组学方法整合了基因组学、转录组学、蛋白质组学、代谢组学和宏基因组学,显示出开发更准确的诊断和风险预测模型、改善治疗反应预测和检测未来治疗中可操作药物靶点的潜力。可穿戴设备和远程监测技术可以将IBD管理从偶发性评估转变为对患者报告的结果和生理生物标志物进行持续、无偏见的跟踪。结论:虽然人工智能和多组学方法在推进IBD管理和研究方面具有巨大的前景,但需要进一步改进以确保内容有效性和解决安全问题,从而将人工智能集成到临床工作流程中并保护数据隐私。未来的研究应优先整合不同的基因组数据,进行纵向研究,并在大型和不同的队列中进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Results of the 9th Scientific Workshop of the European Crohn's and Colitis Organisation (ECCO): Artificial Intelligence in medical management and precision medicine.

Background and aims: Artificial intelligence (AI) is increasingly being applied in various fields of medicine, including Inflammatory Bowel Diseases (IBD). This systematic review, conducted as part of the ECCO 9th Scientific Workshop on AI in IBD, explores AI applications in multiomic precision medicine, large language models (LLMs) for textual tasks and utilisation of wearable and remote care technologies.

Methods: A comprehensive systematic analysis of the literature was undertaken, emphasising three topics: multiomic predictive models in IBD; natural language processing (NLP) and LLMs for clinical practice, research and patient communication; and the role of remote monitoring and wearable devices.

Results: Key areas of promise include the implementation of NLP and LLMs for case identification and differentiation, tracking disease activity, pharmacovigilance, quality assurance and patient support. Multiomic approaches, integrating genomics, transcriptomics, proteomics, metabolomics and metagenomics, show potential for developing more accurate diagnostic and risk prediction models and improving treatment response prediction and detection of actionable drug targets for future therapeutics. Wearables and remote monitoring technologies can transform IBD management from episodic assessments to continuous less biased tracking of patient-reported outcomes and physiological biomarkers.

Conclusions: While AI and multiomic approaches hold substantial promise for advancing IBD management and research, further refinement is necessary to ensure content validity and address safety concerns, thereby allowing integration of AI into clinical workflows and safeguarding of data privacy. Future research should prioritise the integration of diverse omic data, conduct of longitudinal studies and validation in large and diverse cohorts.

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