MaltSci 麦伴科研

Appearance

文献精选 > 高引权威"AI药物发现"文献

Artificial intelligence in healthcare: past, present and future. ​

文献信息 ​

DOI10.1136/svn-2017-000101
PMID29507784
期刊Stroke and vascular neurology
影响因子4.9
JCR 分区Q1
发表年份2017
被引次数964
关键词大数据、深度学习、神经网络、中风、支持向量机
文献类型Historical Article, Journal Article, Review
ISSN2059-8688
页码230-243
期号2(4)
作者Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang

一句话小结 ​

本研究调查了人工智能在医疗保健中的应用现状，发现AI技术（如机器学习和自然语言处理）在处理结构化和非结构化医疗数据方面具有重要作用，尤其在癌症、神经学和心脏病学等领域展现出潜力。重点分析了AI在中风早期检测、治疗和预后评估中的应用，以及当前先锋AI系统面临的现实障碍，为未来AI在医疗领域的进一步发展提供了参考。

在麦伴科研 (maltsci.com) 搜索更多文献 ​

大数据 · 深度学习 · 神经网络 · 中风 · 支持向量机

摘要 ​

人工智能（AI）旨在模拟人类的认知功能。随着医疗数据的日益丰富和分析技术的快速进步，AI正在为医疗保健带来范式转变。我们对AI在医疗保健中的应用现状进行了调查，并讨论了其未来发展。AI可以应用于各种类型的医疗数据（结构化和非结构化）。流行的AI技术包括用于结构化数据的机器学习方法，如经典的支持向量机和神经网络，以及现代的深度学习，以及用于非结构化数据的自然语言处理。使用AI工具的主要疾病领域包括癌症、神经学和心脏病学。接下来，我们将更详细地回顾AI在中风领域的应用，重点关注早期检测与诊断、治疗以及结果预测和预后评估三个主要方面。最后，我们讨论了先锋AI系统，如IBM Watson，以及AI在现实生活中应用所面临的障碍。

英文摘要 ​

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

麦伴智能科研服务 ​

智能阅读回答你对文献的任何问题，帮助理解文献中的复杂图表和公式
定位观点定位某个观点在文献中的蛛丝马迹
加入知识库完成数据提取，报告撰写等更多高级知识挖掘功能

主要研究问题 ​

  1. 人工智能在不同疾病领域（如癌症、神经学和心脏病）的应用效果如何？
  2. 目前在医疗数据分析中，深度学习与传统机器学习方法的优势和劣势分别是什么？
  3. 未来人工智能在医疗行业可能面临哪些伦理和法律挑战？
  4. 如何评估人工智能在早期检测和诊断中的实际效果与准确性？
  5. 有哪些成功案例展示了IBM Watson等先锋AI系统在医疗中的实际应用？

核心洞察 ​

研究背景和目的 ​

人工智能（AI）在医疗领域的应用正在迅速发展，推动着医疗行业的变革。本文旨在回顾AI在医疗中的现状、应用及未来发展，探讨AI如何通过分析结构化和非结构化数据来辅助临床决策，并分析AI在中风等重大疾病领域的应用。

主要方法/材料/实验设计 ​

本文采用文献综述的方式，分析AI在医疗领域的不同应用，重点关注机器学习（ML）和自然语言处理（NLP）技术。以下是研究的主要流程图：

Mermaid diagram
  • 数据类型：AI系统分析的医疗数据包括结构化数据（如医学影像、基因组数据、电子健康记录）和非结构化数据（如临床笔记）。
  • AI技术：主要包括机器学习（传统算法和深度学习）和自然语言处理。机器学习中，支持向量机（SVM）和神经网络是最常用的技术。

关键结果和发现 ​

  1. AI的应用领域：

    • 癌症：AI系统如IBM Watson在癌症诊断中显示出高准确性。
    • 神经系统疾病：AI技术用于恢复四肢瘫痪患者的运动控制。
    • 心血管疾病：AI在心脏影像分析中帮助识别心脏病。

  2. 中风相关应用：

    • 早期检测与诊断：利用机器学习分析运动模式和神经影像数据，提高中风早期识别的准确性。
    • 治疗效果预测：AI模型能预测静脉溶栓治疗的结果，帮助临床决策。
    • 预后评估：AI系统能够基于患者的生理参数和影像数据预测中风后的恢复情况。

主要结论/意义/创新性 ​

AI在医疗中的应用展现出巨大的潜力，能够通过分析海量数据辅助医生做出更准确的诊断和治疗决策。尽管AI技术仍面临法规和数据共享等挑战，但其在提高医疗效率和改善患者预后方面的潜力不可忽视。

研究局限性和未来方向 ​

  • 局限性：当前AI系统在实际应用中受到数据质量、模型训练和临床验证的限制。AI技术的实施还面临法律法规的挑战，尤其是在安全性和有效性评估方面。
  • 未来方向：需要加强AI技术与临床实践的结合，推动数据共享机制，提升AI系统的可用性和可靠性。此外，随着AI技术的不断进步，未来可能会在更多疾病领域实现突破，特别是在个性化医疗和精准医疗方面。

参考文献 ​

  1. Restoring cortical control of functional movement in a human with quadriplegia. - Chad E Bouton;Ammar Shaikhouni;Nicholas V Annetta;Marcia A Bockbrader;David A Friedenberg;Dylan M Nielson;Gaurav Sharma;Per B Sederberg;Bradley C Glenn;W Jerry Mysiw;Austin G Morgan;Milind Deogaonkar;Ali R Rezai - Nature (2016)
  2. Diagnostic errors in the intensive care unit: a systematic review of autopsy studies. - Bradford Winters;Jason Custer;Samuel M Galvagno;Elizabeth Colantuoni;Shruti G Kapoor;Heewon Lee;Victoria Goode;Karen Robinson;Atul Nakhasi;Peter Pronovost;David Newman-Toker - BMJ quality & safety (2012)
  3. The inevitable application of big data to health care. - Travis B Murdoch;Allan S Detsky - JAMA (2013)
  4. Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence. - Hamed Asadi;Hong Kuan Kok;Seamus Looby;Paul Brennan;Alan O'Hare;John Thornton - World neurosurgery (2016)
  5. Integrated analysis of long non-coding RNA competing interactions reveals the potential role in progression of human gastric cancer. - Cheng-Yun Li;Ge-Yu Liang;Wen-Zhuo Yao;Jing Sui;Xian Shen;Yan-Qiu Zhang;Hui Peng;Wei-Wei Hong;Yan-Cheng Ye;Zhi-Yi Zhang;Wen-Hua Zhang;Li-Hong Yin;Yue-Pu Pu - International journal of oncology (2016)
  6. Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. - Joseph C Griffis;Jane B Allendorfer;Jerzy P Szaflarski - Journal of neuroscience methods (2016)
  7. Radiomics: Images Are More than Pictures, They Are Data. - Robert J Gillies;Paul E Kinahan;Hedvig Hricak - Radiology (2016)
  8. Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques. - A K Rehme;L J Volz;D-L Feis;I Bomilcar-Focke;T Liebig;S B Eickhoff;G R Fink;C Grefkes - Cerebral cortex (New York, N.Y. : 1991) (2015)
  9. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. - Varun Gulshan;Lily Peng;Marc Coram;Martin C Stumpe;Derek Wu;Arunachalam Narayanaswamy;Subhashini Venugopalan;Kasumi Widner;Tom Madams;Jorge Cuadros;Ramasamy Kim;Rajiv Raman;Philip C Nelson;Jessica L Mega;Dale R Webster - JAMA (2016)
  10. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. - Konstantinos Kamnitsas;Christian Ledig;Virginia F J Newcombe;Joanna P Simpson;Andrew D Kane;David K Menon;Daniel Rueckert;Ben Glocker - Medical image analysis (2017)

引用本文的文献 ​

  1. Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices. - Minhee Kang;Eunkyoung Park;Baek Hwan Cho;Kyu-Sung Lee - International neurourology journal (2018)
  2. Application of artificial intelligence in ophthalmology. - Xue-Li Du;Wen-Bo Li;Bo-Jie Hu - International journal of ophthalmology (2018)
  3. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. - Jonathan Guo;Bin Li - Health equity (2018)
  4. History and application of artificial neural networks in dentistry. - Wook Joo Park;Jun-Beom Park - European journal of dentistry (2018)
  5. Aging and drug discovery. - Daniela Bakula;Alexander M Aliper;Polina Mamoshina;Michael A Petr;Amanuel Teklu;Joseph A Baur;Judith Campisi;Collin Y Ewald;Anastasia Georgievskaya;Vadim N Gladyshev;Olga Kovalchuk;Dudley W Lamming;Martijn S Luijsterburg;Alejandro Martín-Montalvo;Stuart Maudsley;Garik V Mkrtchyan;Alexey Moskalev;S Jay Olshansky;Ivan V Ozerov;Alexander Pickett;Michael Ristow;Alex Zhavoronkov;Morten Scheibye-Knudsen - Aging (2018)
  6. Big Data Research in Chronic Kidney Disease. - Xiao-Xi Zeng;Jing Liu;Liang Ma;Ping Fu - Chinese medical journal (2018)
  7. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. - Chenxi Huang;Karthik Murugiah;Shiwani Mahajan;Shu-Xia Li;Sanket S Dhruva;Julian S Haimovich;Yongfei Wang;Wade L Schulz;Jeffrey M Testani;Francis P Wilson;Carlos I Mena;Frederick A Masoudi;John S Rumsfeld;John A Spertus;Bobak J Mortazavi;Harlan M Krumholz - PLoS medicine (2018)
  8. Artificial intelligence-enabled healthcare delivery. - Sandeep Reddy;John Fox;Maulik P Purohit - Journal of the Royal Society of Medicine (2019)
  9. Using machine learning to identify health outcomes from electronic health record data. - Jenna Wong;Mara Murray Horwitz;Li Zhou;Sengwee Toh - Current epidemiology reports (2018)
  10. Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning. - Meeshanthini V Dogan;Steven R H Beach;Ronald L Simons;Amaury Lendasse;Brandan Penaluna;Robert A Philibert - Genes (2018)

... (954 更多 篇文献)

© 2025 MaltSci 麦伴科研 - 我们用人工智能技术重塑科研