人工智能在医学领域的应用：医学各领域未来的人工智能前景

English | April 26, 2024 | ISBN: N/A | ASIN: B0D2VHLFRD | 58 pages | EPUB | 0.24 Mb

The modeling of intelligent behavior in computers is the focus of the computer science field of artificial intelligence (AI). These computers use algorithms to support judgments and carry out certain activities. These algorithms are either created by people or taught by the computer. There were significant subfields within AI.

The process through which a computer can reliably incorporate newly-generated data into an iterative model that already exists is known as machine learning. The FDA claims that one of machine learning's possible advantages is its capacity to produce fresh insights from the enormous volume of data produced daily in the course of providing healthcare. Occasionally, we can train a computer to distinguish between benign and malignant pathologies, for example, by using machine learning techniques. To do this, we use annotated datasets that display various images of benign and malignant pathologies. In the end, the machine will provide an algorithm from which we can extract data sets that are no longer classified as benign or malignant. Then, we keep refining and training that algorithm," explained Haddad, a medical oncologist and associate professor of oncology at the Mayo Clinic's Rochester, Minnesota, campus.

Deep learning is a subset of machine learning in which multi-layered computational units that mimic human intellect are used to install mathematical algorithms. These comprise neural networks with various architecture types, such as long short-term memory, convolutional neural networks, and recurrent neural networks.

It can be challenging to determine whether particular AI techniques underpin commercial systems because many of the applications that are integrated into them are proprietary. Simple rules-based systems are still useful for certain applications. But more sophisticated machine learning techniques—particularly neural network-based deep learning, which allows AI to teach itself to recognize patterns in complex data—are largely responsible for the recent acceleration in AI advancements, according to Danielle S. Bitterman, MD, in a statement to Targeted OncologyTM. Deep learning approaches perform better for many applications, however there is a trade-off of. The use of artificial intelligence (AI) is crucial since the human brain's ability to handle information is limited, necessitating the immediate adoption of alternative big data processing techniques. Clinicians can benefit from enhanced data accessibility, as well as greater processing and storage capacity, using machine learning and artificial intelligence. In this book, we will explore the applications of AI in various medical fields and delve into its advantages and limitations.

计算机模拟智能行为是计算机科学领域人工智能（AI）的研究焦点。这些计算机使用算法来支持判断并执行某些活动。这些算法要么是由人类创建的，要么是通过计算机自我教出来的。人工智能（AI）领域内还有许多重要分支。 机器学习的过程允许计算机可靠地将新生成的数据纳入已经存在的迭代模型中。FDA指出，机器学习的一个潜在优势是可以从医疗保健过程中每天产生的大量数据中产生新的见解。 例如，有时我们可以通过使用机器学习技术来训练计算机区分良性和恶性病变。为此，我们会使用包含不同良性及恶性病变图像的标注数据集。最终，机器将提供一种算法，从中我们可以提取不再被分类为良性或恶性的新数据集。然后，我们继续不断优化和训练这种算法。 梅奥诊所罗切斯特分校医学肿瘤学助理教授Haddad解释说。 深度学习是机器学习的一个子领域，在此过程中会使用多层计算单元来植入模仿人类智能的数学算法。这些包括各种架构类型的神经网络，如长短时记忆、卷积神经网络和递归神经网络。 确定某些AI技术是否支撑商用系统很困难，因为集成到它们中的许多应用都是专有化的。对于某些应用而言，基于规则的简单系统依然有用。但是，根据德内拉·梅奥（Danielle S. Bhubman）医生在《靶向肿瘤学》杂志上发表的一篇文章所述，更复杂的机器学习技术——特别是神经网络为基础的深度学习技术，能够让AI自主识别复杂数据中的模式——正是近年来AI进步的主要推动力。 尽管许多应用领域中深度学习方法表现更好，但也存在一些权衡。使用人工智能（AI）至关重要，因为人类大脑处理信息的能力有限，因此需要立即采用替代的大数据分析技术。临床医生可以受益于增强的数据访问性、更好的数据处理和存储能力，通过机器学习和人工智能。

---

Download from free file storage

 Download from rapidgator.net  Download from nitroflare.com

---

我要反馈

---

本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。