Alsuliman Tamim, Humaidan Dania, Sliman Layth
Hematology and Cell Therapy Department, Saint-Antoine Hospital, AP-HP, Sorbonne University, Paris, France.
Cognitive Modeling Group, Department of Computer Science, Tubingen University and Max Planck Research School for Intelligent Systems, Tubingen, Germany.
Curr Res Transl Med. 2020 Nov;68(4):245-251. doi: 10.1016/j.retram.2020.01.002. Epub 2020 Feb 3.
As a result of the worldwide health care system digitalization trend, the produced healthcare data is estimated to reach as much as 2314 Exabytes of new data generated in 2020. The ongoing development of intelligent systems aims to provide better reasoning and to more efficiently use the data collected. This use is not restricted retrospective interpretation, that is, to provide diagnostic conclusions. It can also be extended to prospective interpretation providing early prognosis. That said, physicians who could be assisted by these systems find themselves standing in the gap between clinical case and deep technical reviews. What they lack is a clear starting point from which to approach the world of machine learning in medicine.
This article aims at providing interested physicians with an easy-to-follow insight of Artificial Intelligence (AI) and Machine Learning (ML) use in the medical field, primarily over the last few years. To this end, we first discuss the general developmental paths concerning AI and ML concept usage in healthcare systems. We then list fields where these technologies are already being put to the test or even applied such as in Hematology, Neurology, Cardiology, Oncology, Radiology, Ophthalmology, Cell Biology and Cell Therapy.
由于全球医疗保健系统数字化趋势，预计2020年产生的医疗数据将高达2314艾字节新数据。智能系统的不断发展旨在提供更好的推理，并更有效地利用收集到的数据。这种使用不仅限于回顾性解释，即提供诊断结论。它还可以扩展到提供早期预后的前瞻性解释。也就是说，可能会得到这些系统辅助的医生发现自己处于临床病例与深入技术评估之间的差距之中。他们所缺乏的是一个清晰的起点，以便进入医学机器学习领域。
本文旨在为感兴趣的医生提供对人工智能（AI）和机器学习（ML）在医疗领域应用的易于理解的见解，主要是过去几年的情况。为此，我们首先讨论医疗保健系统中人工智能和机器学习概念使用的一般发展路径。然后，我们列出这些技术已经在接受测试甚至应用的领域，如血液学、神经病学、心脏病学、肿瘤学、放射学、眼科、细胞生物学和细胞治疗。
