Literature DB >> 28651747

The rise of artificial intelligence and the uncertain future for physicians.

C Krittanawong1.   

Abstract

Physicians in everyday clinical practice are under pressure to innovate faster than ever because of the rapid, exponential growth in healthcare data. "Big data" refers to extremely large data sets that cannot be analyzed or interpreted using traditional data processing methods. In fact, big data itself is meaningless, but processing it offers the promise of unlocking novel insights and accelerating breakthroughs in medicine-which in turn has the potential to transform current clinical practice. Physicians can analyze big data, but at present it requires a large amount of time and sophisticated analytic tools such as supercomputers. However, the rise of artificial intelligence (AI) in the era of big data could assist physicians in shortening processing times and improving the quality of patient care in clinical practice. This editorial provides a glimpse at the potential uses of AI technology in clinical practice and considers the possibility of AI replacing physicians, perhaps altogether. Physicians diagnose diseases based on personal medical histories, individual biomarkers, simple scores (e.g., CURB-65, MELD), and their physical examinations of individual patients. In contrast, AI can diagnose diseases based on a complex algorithm using hundreds of biomarkers, imaging results from millions of patients, aggregated published clinical research from PubMed, and thousands of physician's notes from electronic health records (EHRs). While AI could assist physicians in many ways, it is unlikely to replace physicians in the foreseeable future. Let us look at the emerging uses of AI in medicine.
Copyright © 2017 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Big data; Precision medicine

Mesh:

Year:  2017        PMID: 28651747     DOI: 10.1016/j.ejim.2017.06.017

Source DB:  PubMed          Journal:  Eur J Intern Med        ISSN: 0953-6205            Impact factor:   4.487


  25 in total

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Review 2.  Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.

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3.  Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation.

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Review 4.  Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review.

Authors:  Vatsal Patel; Marium N Khan; Aman Shrivastava; Kamran Sadiq; S Asad Ali; Sean R Moore; Donald E Brown; Sana Syed
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5.  Artificial intelligence in radiology: Are Saudi residents ready, prepared, and knowledgeable?

Authors:  Mawya A Khafaji; Mohammed A Safhi; Roia H Albadawi; Salma O Al-Amoudi; Salah S Shehata; Fadi Toonsi
Journal:  Saudi Med J       Date:  2022-01       Impact factor: 1.422

Review 6.  Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.

Authors:  Dennis Jay Wong; Ziba Gandomkar; Wan-Jing Wu; Guijing Zhang; Wushuang Gao; Xiaoying He; Yunuo Wang; Warren Reed
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Review 7.  Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States.

Authors:  Filippo Pesapane; Caterina Volonté; Marina Codari; Francesco Sardanelli
Journal:  Insights Imaging       Date:  2018-08-15

8.  Physician Confidence in Artificial Intelligence: An Online Mobile Survey.

Authors:  Songhee Oh; Jae Heon Kim; Sung-Woo Choi; Hee Jeong Lee; Jungrak Hong; Soon Hyo Kwon
Journal:  J Med Internet Res       Date:  2019-03-25       Impact factor: 5.428

9.  The impact of artificial intelligence in medicine on the future role of the physician.

Authors:  Abhimanyu S Ahuja
Journal:  PeerJ       Date:  2019-10-04       Impact factor: 2.984

Review 10.  Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.

Authors:  Filippo Pesapane; Marina Codari; Francesco Sardanelli
Journal:  Eur Radiol Exp       Date:  2018-10-24
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