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人工智能时代临床研究者的历程:吃豆人的挑战。

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN's Challenge.

作者信息

Bignami Elena Giovanna, Vittori Alessandro, Lanza Roberto, Compagnone Christian, Cascella Marco, Bellini Valentina

机构信息

Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.

Department of Anesthesia and Critical Care, ARCO ROMA, Ospedale Pediatrico Bambino Gesù IRCCS, Piazza S. Onofrio 4, 00165 Rome, Italy.

出版信息

Healthcare (Basel). 2023 Mar 29;11(7):975. doi: 10.3390/healthcare11070975.


DOI:10.3390/healthcare11070975
PMID:37046900
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10093965/
Abstract

Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by "ghosts", which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

摘要

人工智能(AI)是一种强大的工具,可在各种环境中协助研究人员和临床医生。然而,与任何技术一样,必须谨慎使用并充分认识到它,因为存在许多潜在的陷阱。为了提供一个有创意的类比,我们将研究比作经典街机视频游戏《吃豆人》。就像游戏主角不断寻找数据一样,研究人员也在不断寻找信息,这些信息必须在研究规则的约束下获取和管理。在我们的类比中,研究人员面临的障碍由“幽灵”代表,它们象征着主要的伦理问题、低质量数据、法律问题和教育挑战。简而言之,临床研究人员需要精心收集和分析来自各种来源的数据,经常要应对复杂而细微的挑战,以确保他们获得的数据既精确又与他们的研究问题相关。思考这个类比可以促进对提高意识和关注度来使用人工智能及其他强大技术的重要性有更深刻的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb84/10093965/b13ad3ee306e/healthcare-11-00975-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb84/10093965/3ebea147af30/healthcare-11-00975-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb84/10093965/b13ad3ee306e/healthcare-11-00975-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb84/10093965/3ebea147af30/healthcare-11-00975-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb84/10093965/b13ad3ee306e/healthcare-11-00975-g002.jpg

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The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN's Challenge.

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本文引用的文献

[1]
Health Informatics: The Foundations of Public Health.

Healthcare (Basel). 2023-3-8

[2]
A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia.

Healthcare (Basel). 2023-2-26

[3]
On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories.

Healthcare (Basel). 2023-2-24

[4]
Recent advancements in digital health management using multi-modal signal monitoring.

Math Biosci Eng. 2023-1-9

[5]
Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios.

J Med Syst. 2023-3-4

[6]
Ethics and governance of trustworthy medical artificial intelligence.

BMC Med Inform Decis Mak. 2023-1-13

[7]
Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.

J Med Internet Res. 2023-1-10

[8]
Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey.

World J Emerg Surg. 2023-1-3

[9]
Recent advances in Predictive Learning Analytics: A decade systematic review (2012-2022).

Educ Inf Technol (Dordr). 2022-12-20

[10]
Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid.

Eur Radiol. 2023-5

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