The Medical School, University of Sheffield, Sheffield, UK.
Lancaster Medical School, Furness College, Lancaster University, Bailrigg, Lancaster, UK.
EBioMedicine. 2019 Sep;47:607-615. doi: 10.1016/j.ebiom.2019.08.027. Epub 2019 Aug 26.
Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
对于希望产生临床影响的实验室科学家来说，大数据问题变得越来越普遍。这在很大程度上是由于计算能力的提高，以及高质量数据生成的新技术。人工智能 (AI) 和机器学习 (ML) 的新技术和旧技术现在都可以帮助提高药物发现、成像和基因组医学三个领域的转化研究的成功率。然而，机器学习技术并非没有局限性和缺点。本文将讨论当前的技术限制和其他限制，包括治理、可重复性和解释。克服这些限制将使 ML 方法在发现方面更加强大，并减少转化医学中的歧义，使基于数据的决策能够更快、以更低的成本和更大的规模为患者提供下一代诊断和治疗方法。
