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Revolutionizing healthcare: the role of artificial intelligence in clinical practice. ​

文献信息 ​

DOI10.1186/s12909-023-04698-z
PMID37740191
期刊BMC medical education
影响因子3.2
JCR 分区Q1
发表年份2023
被引次数600
关键词人工智能、临床医生、决策制定、医疗保健、患者护理
文献类型Journal Article, Review
ISSN1472-6920
页码689
期号23(1)
作者Shuroug A Alowais, Sahar S Alghamdi, Nada Alsuhebany, Tariq Alqahtani, Abdulrahman I Alshaya, Sumaya N Almohareb, Atheer Aldairem, Mohammed Alrashed, Khalid Bin Saleh, Hisham A Badreldin, Majed S Al Yami, Shmeylan Al Harbi, Abdulkareem M Albekairy

一句话小结 ​

本文综述了人工智能在临床实践中的应用现状，指出其在疾病诊断、治疗建议和患者参与等方面具有显著潜力，同时也面临伦理、法律和人类专业知识需求等挑战。研究强调了有效整合人工智能技术的重要性，以改善医疗服务质量并推动个性化医疗的发展。

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人工智能 · 临床医生 · 决策制定 · 医疗保健 · 患者护理

摘要 ​

引言
医疗保健系统对所有利益相关者来说都是复杂而具有挑战性的，但人工智能（AI）已经在包括医疗保健在内的多个领域发生了变革，具有改善患者护理和生活质量的潜力。快速发展的人工智能有可能通过将其融入临床实践来彻底改变医疗保健。报告人工智能在临床实践中的作用对于成功实施至关重要，这将使医疗服务提供者具备必要的知识和工具。

研究意义
本文综述了人工智能在临床实践中的现状，提供了全面而最新的概述，包括其在疾病诊断、治疗建议和患者参与方面的潜在应用。它还讨论了相关挑战，涵盖伦理和法律考量以及对人类专业知识的需求。通过这样做，增强了对人工智能在医疗保健中重要性的理解，并支持医疗组织有效采用人工智能技术。

材料与方法
本研究分析了人工智能在医疗保健系统中的应用，进行了相关索引文献的全面回顾，如PubMed/Medline、Scopus和EMBASE，时间上没有限制，但仅限于发表在英语的文章。研究的重点问题探讨了在医疗环境中应用人工智能的影响及其潜在结果。

结果
将人工智能融入医疗保健具有极大的潜力，可以改善疾病诊断、治疗选择和临床实验室检测。人工智能工具能够利用大数据集并识别模式，在多个医疗保健方面超越人类表现。人工智能提供了更高的准确性、降低的成本和节省的时间，同时减少人为错误。它能够彻底改变个性化医疗，优化药物剂量，增强人口健康管理，建立指导方针，提供虚拟健康助手，支持心理健康护理，改善患者教育，并影响患者与医生之间的信任。

结论
人工智能可以用于诊断疾病，制定个性化治疗方案，并协助临床医生进行决策。人工智能不仅仅是自动化任务，而是开发可以在各种医疗环境中增强患者护理的技术。然而，与数据隐私、偏见以及对人类专业知识需求相关的挑战必须得到解决，以确保人工智能在医疗保健中负责任和有效的实施。

英文摘要 ​

INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

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主要研究问题 ​

  1. 人工智能在临床实践中的具体应用案例有哪些？它们如何影响患者的治疗效果？
  2. 在人工智能技术的实施过程中，医疗机构面临哪些主要挑战，特别是在伦理和法律方面？
  3. 如何评估人工智能在个性化医疗中的效果与传统治疗方法的差异？
  4. 人工智能如何改变患者与医疗提供者之间的互动，尤其是在患者教育和参与方面？
  5. 未来人工智能在临床实践中的发展趋势是什么？有哪些新兴技术可能会被整合进医疗系统？

核心洞察 ​

研究背景和目的 ​

人工智能（AI）在医疗保健领域的应用正在迅速发展，具有改善患者护理和生活质量的潜力。本研究旨在提供AI在临床实践中的当前状态及其潜在应用的全面概述，探讨其在疾病诊断、治疗建议和患者参与中的应用，并讨论相关的伦理和法律挑战。

主要方法/材料/实验设计 ​

本研究采用文献综述的方法，分析了在医疗系统中AI的应用，文献来源包括PubMed/Medline、Scopus和EMBASE等数据库。研究关注AI在医疗环境中的应用影响及其潜在结果。

Mermaid diagram

关键结果和发现 ​

  1. 疾病诊断：AI工具能够利用大数据集识别模式，提升疾病诊断的准确性。例如，AI在乳腺癌诊断中的表现优于放射科医师。
  2. 治疗建议：AI可用于个性化治疗计划的制定，预测患者对不同治疗的反应。
  3. 临床实验室测试：AI在临床微生物学中显示出高效性，能够快速准确地检测微生物并预测临床结果。
  4. 患者管理：AI在急诊部门的应用能够优化患者流动，提高效率，减少等待时间。

主要结论/意义/创新性 ​

AI的整合有潜力彻底改变医疗服务，提供更高效、更准确的医疗服务。然而，实施AI时必须解决数据隐私、偏见和人类专业知识的需求等挑战。AI的成功应用将促进个性化医学的发展，提高患者的健康管理和治疗效果。

研究局限性和未来方向 ​

  1. 局限性：文献综述的结果可能受到选择偏倚的影响，且AI技术在临床应用中的成熟度尚待提高。
  2. 未来方向：需要进行更大规模的前瞻性研究，以验证AI在不同医疗环境中的应用效果。同时，应加强对AI技术的伦理和法律监管，确保其安全和有效的实施。
研究方向现状未来方向
AI在疾病诊断中的应用显著提高准确性进一步研究多种疾病的AI应用
个性化治疗发展迅速，但需更多临床验证加强与临床实践的结合
数据隐私与伦理亟待解决的挑战制定相关法规和指导原则

AI在医疗保健中的潜力巨大，但需要多方合作以克服当前面临的挑战，确保其在提高医疗质量和效率方面的有效应用。

参考文献 ​

  1. A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning. - Ke Han;Peigang Cao;Yu Wang;Fang Xie;Jiaqi Ma;Mengyao Yu;Jianchun Wang;Yaoqun Xu;Yu Zhang;Jie Wan - Frontiers in pharmacology (2021)
  2. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. - Alexandre Blanco-González;Alfonso Cabezón;Alejandro Seco-González;Daniel Conde-Torres;Paula Antelo-Riveiro;Ángel Piñeiro;Rebeca Garcia-Fandino - Pharmaceuticals (Basel, Switzerland) (2023)
  3. Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells. - Taesik Go;Jun H Kim;Hyeokjun Byeon;Sang J Lee - Journal of biophotonics (2018)
  4. Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD). - Julia Yuen Hang Liu;John A Rudd - Scientific reports (2023)
  5. An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study. - Magdalena Görtz;Kilian Baumgärtner;Tamara Schmid;Marc Muschko;Philipp Woessner;Axel Gerlach;Michael Byczkowski;Holger Sültmann;Stefan Duensing;Markus Hohenfellner - Digital health (2023)
  6. Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0. - Petar Radanliev;David De Roure - Health and technology (2023)
  7. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. - Thi Tuyet Van Tran;Agung Surya Wibowo;Hilal Tayara;Kil To Chong - Journal of chemical information and modeling (2023)
  8. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. - Ling-Rui Li;Bo Du;Han-Qing Liu;Chuang Chen - Frontiers in oncology (2020)
  9. Implementing electronic health care predictive analytics: considerations and challenges. - Ruben Amarasingham;Rachel E Patzer;Marco Huesch;Nam Q Nguyen;Bin Xie - Health affairs (Project Hope) (2014)
  10. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. - Sarah Graham;Colin Depp;Ellen E Lee;Camille Nebeker;Xin Tu;Ho-Cheol Kim;Dilip V Jeste - Current psychiatry reports (2019)

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  2. Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models. - Malik Sallam;Muna Barakat;Mohammed Sallam - Cureus (2023)
  3. The leading global health challenges in the artificial intelligence era. - Amal Mousa Zaidan - Frontiers in public health (2023)
  4. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. - Luís Pinto-Coelho - Bioengineering (Basel, Switzerland) (2023)
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  8. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients. - Simon W P Temple;Carl G Rowbottom - Journal of applied clinical medical physics (2024)
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