Transforming precision medicine: The potential of the clinical artificial intelligent single-cell framework

IF 7.9 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Clinical and Translational Medicine Pub Date : 2025-01-06 DOI:10.1002/ctm2.70096

Christian Baumgartner, Dagmar Brislinger

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引用次数: 0

Abstract

The editorial, “Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell,” introduces the innovative clinical artificial intelligence single-cell (caiSC) system, which merges AI with single-cell informatics to advance real-time diagnostics, disease monitoring, and treatment prediction. By combining clinical data and multimodal molecular inputs, caiSC facilitates personalized medicine, promising enhanced diagnostic precision and tailored therapeutic approaches. Despite its potential, caiSC lacks comprehensive data coverage across cell types and diseases, presenting challenges in data quality and model robustness. The article explores development strategies such as data expansion, machine learning advancements, and interpretability improvements. Future applications of caiSC could include digital cell twins, offering in-depth simulations of cellular behavior to support drug discovery and personalized treatments. Regulatory considerations are discussed, underscoring the need for SaMD/AIaMD certifications for clinical use. Ultimately, with further refinement, caiSC could transform clinical decision-making, driving personalized, precision medicine, and improved patient outcomes.

Key points

Integration of AI with Single-Cell Informatics for Precision Medicine: The caiSC system combines artificial intelligence and single-cell data to improve diagnostics, treatment predictions, and personalized medical decision-making.
Challenges in Data Coverage and Model Robustness: caiSC currently faces limitations due to incomplete data across cell types, diseases, and organs, as well as challenges in data quality and high computational demands, which affect model accuracy and clinical applicability.
Future Potential and Regulatory Needs: The caiSC framework's development could lead to innovations such as digital cell twins, enabling personalized simulations of cellular responses for better treatment planning, though regulatory certification is essential for safe clinical use.

Abstract Image

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改变精准医疗：临床人工智能单细胞框架的潜力。

这篇题为《单细胞测量的临床和转化模式：人工智能单细胞》的社论介绍了创新的临床人工智能单细胞（caiSC）系统，该系统将人工智能与单细胞信息学相结合，以推进实时诊断、疾病监测和治疗预测。通过结合临床数据和多模态分子输入，caiSC促进了个性化医疗，有望提高诊断精度和定制治疗方法。尽管具有潜力，但caiSC缺乏对细胞类型和疾病的全面数据覆盖，这在数据质量和模型稳健性方面提出了挑战。本文探讨了数据扩展、机器学习进步和可解释性改进等开发策略。caiSC的未来应用可能包括数字细胞双胞胎，提供细胞行为的深入模拟，以支持药物发现和个性化治疗。讨论了监管方面的考虑，强调了临床使用SaMD/AIaMD认证的必要性。最终，通过进一步完善，caiSC可以改变临床决策，推动个性化、精准医疗，并改善患者的治疗效果。重点：人工智能与单细胞信息的集成，用于精准医疗：caiSC系统将人工智能与单细胞数据相结合，以提高诊断，治疗预测和个性化医疗决策。数据覆盖和模型鲁棒性方面的挑战：由于细胞类型、疾病和器官数据不完整，以及数据质量和高计算需求方面的挑战，caiSC目前面临局限性，影响了模型的准确性和临床适用性。未来的潜力和监管需求：caiSC框架的发展可能会导致创新，如数字细胞双胞胎，使细胞反应的个性化模拟更好的治疗计划，尽管监管认证是安全临床使用的必要条件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical and Translational Medicine Multiple-

CiteScore

15.90

自引率

1.90%

发文量

450

审稿时长

4 weeks

期刊介绍： Clinical and Translational Medicine (CTM) is an international, peer-reviewed, open-access journal dedicated to accelerating the translation of preclinical research into clinical applications and fostering communication between basic and clinical scientists. It highlights the clinical potential and application of various fields including biotechnologies, biomaterials, bioengineering, biomarkers, molecular medicine, omics science, bioinformatics, immunology, molecular imaging, drug discovery, regulation, and health policy. With a focus on the bench-to-bedside approach, CTM prioritizes studies and clinical observations that generate hypotheses relevant to patients and diseases, guiding investigations in cellular and molecular medicine. The journal encourages submissions from clinicians, researchers, policymakers, and industry professionals.