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The potential for artificial intelligence in healthcare. ​

文献信息 ​

DOI10.7861/futurehosp.6-2-94
PMID31363513
期刊Future healthcare journal
发表年份2019
被引次数842
关键词人工智能、临床决策支持、电子健康记录系统
文献类型Case Reports, Journal Article
ISSN2514-6645
页码94-98
期号6(2)
作者Thomas Davenport, Ravi Kalakota

一句话小结 ​

随着医疗保健领域数据的复杂性与增长，人工智能的应用日益广泛，涵盖诊断、治疗建议、患者参与及行政活动等方面。尽管AI在执行医疗任务上表现优异，但实施障碍和伦理问题仍将限制其大规模自动化的进程。

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人工智能 · 临床决策支持 · 电子健康记录系统

摘要 ​

医疗保健领域数据的复杂性和增长意味着人工智能（AI）将在该领域得到越来越广泛的应用。目前，支付方、护理提供者和生命科学公司已经在使用几种类型的AI。主要的应用类别包括诊断和治疗建议、患者参与与依从性以及行政活动。尽管在许多情况下，AI能够与人类同样或更好地执行医疗任务，但实施因素将在相当长的一段时间内阻止医疗专业工作的大规模自动化。此外，关于AI在医疗保健应用中的伦理问题也进行了讨论。

英文摘要 ​

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

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主要研究问题 ​

  1. 人工智能在医疗诊断和治疗推荐中的具体应用案例有哪些？
  2. 在患者参与和依从性方面，人工智能如何改善患者的健康管理？
  3. 实施人工智能技术时，医疗行业面临哪些伦理挑战？
  4. 人工智能在医疗行政活动中的应用如何影响医疗服务的效率？
  5. 当前人工智能在生命科学公司中的应用趋势是什么？

核心洞察 ​

研究背景和目的 ​

随着医疗数据的复杂性和数量的增加，人工智能（AI）在医疗领域的应用日益增多。本文旨在探讨AI在医疗保健中的潜力，包括其在诊断、治疗推荐、患者参与和行政管理等方面的应用，同时分析其实施面临的挑战和伦理问题。

主要方法/材料/实验设计 ​

本研究通过文献回顾和现有AI技术的分析，探讨了不同类型的AI在医疗中的应用。以下是主要技术路线的流程图：

Mermaid diagram

关键结果和发现 ​

  1. 机器学习与深度学习：AI在医疗中的应用主要集中在精准医学、影像分析和患者行为预测等方面。深度学习在癌症影像识别中的应用显示出较高的准确性。
  2. 自然语言处理：AI能够分析临床记录，生成报告，并进行患者交互，但在处理复杂健康问题时存在一定局限性。
  3. 行政管理：AI技术如机器人流程自动化（RPA）在提高医疗管理效率方面表现良好，能够处理索赔处理和临床文档管理等重复性任务。
  4. 伦理问题：AI的应用引发了关于透明度、责任和算法偏见的伦理讨论。

主要结论/意义/创新性 ​

AI在医疗保健中展现出巨大的潜力，能够改善诊断和治疗的准确性，提升患者参与度，并优化行政流程。尽管AI技术的成熟速度较快，但在临床实践中的广泛应用仍需克服技术集成、监管审批和临床工作流程适应等多重挑战。

研究局限性和未来方向 ​

  1. 局限性：当前AI在医疗中的应用仍处于初级阶段，许多技术尚未被广泛采纳，特别是在临床决策支持和患者互动方面。
  2. 未来方向：未来的研究应集中在提升AI系统的集成能力、降低实施成本以及确保伦理合规性上。同时，随着技术的发展，AI可能会在个性化医疗和精准医学中发挥更大作用。

总体而言，AI在医疗领域的未来充满希望，但其成功实施需要跨学科的合作与持续的政策支持。

参考文献 ​

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  5. Emergence of New Disease: How Can Artificial Intelligence Help? - Yurim Park;Daniel Casey;Indra Joshi;Jiming Zhu;Feng Cheng - Trends in molecular medicine (2020)
  6. Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing. - Debbie Rankin;Michaela Black;Raymond Bond;Jonathan Wallace;Maurice Mulvenna;Gorka Epelde - JMIR medical informatics (2020)
  7. Artificial Intelligence in Modern Medicine - The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care. - Pradnya Brijmohan Bhattad;Vinay Jain - Cureus (2020)
  8. Factors Associated with 5-Year Costs of Care among a Cohort of Alcohol Use Disorder Patients: A Bayesian Network Model. - Elina Rautiainen;Olli-Pekka Ryynänen;Tiina Laatikainen;Pekka Kekolahti - Healthcare informatics research (2020)
  9. Shooting from the hip into our own foot? A perspective on how artificial intelligence may disrupt medical training. - Anmol Arora - Future healthcare journal (2020)
  10. Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. - Onur Asan;Alparslan Emrah Bayrak;Avishek Choudhury - Journal of medical Internet research (2020)

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