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Artificial intelligence in healthcare. ​

Literature Information ​

DOI10.1038/s41551-018-0305-z
PMID31015651
JournalNature biomedical engineering
Impact Factor26.6
JCR QuartileQ1
Publication Year2018
Times Cited771
KeywordsArtificial Intelligence, Medical Practice, Machine Learning, Biomedical Applications, Economic and Social Implications
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
ISSN2157-846X
Pages719-731
Issue2(10)
AuthorsKun-Hsing Yu, Andrew L Beam, Isaac S Kohane

TL;DR ​

This review article discusses the transformative impact of artificial intelligence (AI) on medical practice, highlighting recent advancements in AI technologies and their applications in biomedicine. It also addresses the challenges for future development of medical AI systems and explores the economic, legal, and social implications of AI in healthcare, underscoring its significance in revolutionizing patient care and clinical decision-making.

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Artificial Intelligence · Medical Practice · Machine Learning · Biomedical Applications · Economic and Social Implications

Abstract ​

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

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Primary Questions Addressed ​

  1. What are the specific AI technologies currently being implemented in healthcare settings?
  2. How do AI applications in healthcare compare to traditional methods in terms of efficiency and accuracy?
  3. What ethical considerations arise from the use of AI in patient care and decision-making processes?
  4. How can healthcare providers address the challenges of integrating AI systems into existing workflows?
  5. What are the potential economic impacts of AI adoption on healthcare costs and patient outcomes?

Key Findings ​

Key Insights on "Artificial Intelligence in Healthcare"

  1. Research Background and Purpose:
    The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical practice. Recent advancements in digitized data acquisition, machine learning, and computing capabilities have opened up new avenues for AI applications in areas traditionally dominated by human expertise. This review article aims to elucidate the significant breakthroughs in AI technologies relevant to biomedicine, while also addressing the challenges that impede further advancement of medical AI systems. Additionally, it seeks to explore the broader economic, legal, and social implications of AI deployment in healthcare settings.

  2. Main Methods and Findings:
    The article synthesizes recent literature and case studies that highlight the successful implementation of AI technologies in various medical domains. Key findings indicate that AI is being utilized for tasks such as diagnosis, treatment recommendations, patient monitoring, and operational efficiencies within healthcare systems. Breakthroughs include improved algorithms for imaging analysis, predictive analytics for patient outcomes, and natural language processing for managing clinical data. However, the review also identifies significant challenges, including data privacy concerns, algorithmic bias, and the need for regulatory frameworks to ensure safe and ethical AI use in clinical environments.

  3. Core Conclusions:
    The review concludes that while AI holds immense potential to enhance healthcare delivery and improve patient outcomes, realizing this potential requires addressing several critical challenges. These include the development of transparent AI systems that clinicians can trust, effective integration of AI tools into existing workflows, and the establishment of regulatory standards that safeguard patient rights and data privacy. The successful adoption of AI in healthcare will depend on a collaborative approach involving technologists, healthcare professionals, and policymakers.

  4. Research Significance and Impact:
    This review underscores the transformative impact of AI on healthcare, suggesting that it could lead to more personalized, efficient, and effective medical care. The insights provided serve as a foundation for stakeholders in the healthcare sector to understand both the opportunities and the hurdles associated with AI integration. The economic implications are significant, as AI has the potential to reduce costs and improve resource allocation in healthcare systems. Moreover, the legal and social implications raise important questions about accountability, equity, and access to AI-driven healthcare solutions. Ultimately, this research highlights the urgent need for multidisciplinary collaboration to harness AI's full potential while ensuring ethical standards are upheld in its application.

Literatures Citing This Work ​

  1. Point of Care Sensing Devices: Better Care for Everyone. - Ajeet Kaushik;Mubarak A Mujawar - Sensors (Basel, Switzerland) (2018)
  2. Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System. - Quan-Hoang Vuong;Manh-Tung Ho;Thu-Trang Vuong;Viet-Phuong La;Manh-Toan Ho;Kien-Cuong P Nghiem;Bach Xuan Tran;Hai-Ha Giang;Thu-Vu Giang;Carl Latkin;Hong-Kong T Nguyen;Cyrus S H Ho;Roger C M Ho - Journal of clinical medicine (2019)
  3. Preparing next-generation scientists for biomedical big data: artificial intelligence approaches. - Jason H Moore;Mary Regina Boland;Pablo G Camara;Hannah Chervitz;Graciela Gonzalez;Blanca E Himes;Dokyoon Kim;Danielle L Mowery;Marylyn D Ritchie;Li Shen;Ryan J Urbanowicz;John H Holmes - Personalized medicine (2019)
  4. Artificial intelligence for precision oncology: beyond patient stratification. - Francisco Azuaje - NPJ precision oncology (2019)
  5. Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. - Bach Xuan Tran;Giang Thu Vu;Giang Hai Ha;Quan-Hoang Vuong;Manh-Tung Ho;Thu-Trang Vuong;Viet-Phuong La;Manh-Toan Ho;Kien-Cuong P Nghiem;Huong Lan Thi Nguyen;Carl A Latkin;Wilson W S Tam;Ngai-Man Cheung;Hong-Kong T Nguyen;Cyrus S H Ho;Roger C M Ho - Journal of clinical medicine (2019)
  6. Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners' Views. - Charlotte Blease;Ted J Kaptchuk;Michael H Bernstein;Kenneth D Mandl;John D Halamka;Catherine M DesRoches - Journal of medical Internet research (2019)
  7. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. - Farah Magrabi;Elske Ammenwerth;Jytte Brender McNair;Nicolet F De Keizer;Hannele Hyppönen;Pirkko Nykänen;Michael Rigby;Philip J Scott;Tuulikki Vehko;Zoie Shui-Yee Wong;Andrew Georgiou - Yearbook of medical informatics (2019)
  8. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. - Jason H Yang;Sarah N Wright;Meagan Hamblin;Douglas McCloskey;Miguel A Alcantar;Lars Schrübbers;Allison J Lopatkin;Sangeeta Satish;Amir Nili;Bernhard O Palsson;Graham C Walker;James J Collins - Cell (2019)
  9. Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds. - Matthew Boubin;Sudhir Shrestha - Sensors (Basel, Switzerland) (2019)
  10. KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images. - Isidro Cortés-Ciriano;Andreas Bender - Journal of cheminformatics (2019)

... (761 more literatures)

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