AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database Article Swipe
Wanling Gao
,
Yuan Liu
,
Zhuoming Yu
,
Dandan Cui
,
Wenjing Liu
,
Xiaoshuang Liang
,
Jiahui Zhao
,
Jiyue Xie
,
Hao Li
,
Li Ma
,
Ning Ye
,
Yumiao Kang
,
Dingfeng Luo
,
Linqiang Pan
,
Wei Huang
,
Zhongmou Liu
,
Jizhong Hu
,
Fan Huang
,
Gangyuan Zhao
,
Chongrong Jiang
,
Tianyi Wei
,
Zhifei Zhang
,
Yunyou Huang
,
Jianfeng Zhan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.07362
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.07362
Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicians assuming a dominant role. Unfortunately, the intricate dynamics and interactions between AI and clinicians remain undiscovered and thus hinder AI from being translated into medical practice. To address this gap, we have curated a groundbreaking database called AI.vs.Clinician. This database is the first of its kind for studying the interactions between AI and clinicians. It derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency -- Sepsis -- from 14 medical centers across China. For the patient cohorts well-chosen from MIMIC databases, the AI-related information comprises the model property, feature input, diagnosis decision, and inferred probabilities of sepsis onset presently and within next three hours. The clinician-related information includes the viewed examination data and sequence, viewed time, preliminary and final diagnosis decisions with or without AI assistance, and recommended treatment.
Related Topics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.07362
- https://arxiv.org/pdf/2406.07362
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399598022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399598022Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.07362Digital Object Identifier
- Title
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AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access DatabaseWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-06-11Full publication date if available
- Authors
-
Wanling Gao, Yuan Liu, Zhuoming Yu, Dandan Cui, Wenjing Liu, Xiaoshuang Liang, Jiahui Zhao, Jiyue Xie, Hao Li, Li Ma, Ning Ye, Yumiao Kang, Dingfeng Luo, Linqiang Pan, Wei Huang, Zhongmou Liu, Jizhong Hu, Fan Huang, Gangyuan Zhao, Chongrong Jiang, Tianyi Wei, Zhifei Zhang, Yunyou Huang, Jianfeng ZhanList of authors in order
- Landing page
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https://arxiv.org/abs/2406.07362Publisher landing page
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https://arxiv.org/pdf/2406.07362Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2406.07362Direct OA link when available
- Concepts
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Computer science, Database, World Wide Web, Data science, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.between | 31, 48, 88 |
| abstract_inverted_index.centers | 110 |
| abstract_inverted_index.cohorts | 116 |
| abstract_inverted_index.crucial | 5 |
| abstract_inverted_index.curated | 70 |
| abstract_inverted_index.derives | 93 |
| abstract_inverted_index.feature | 128 |
| abstract_inverted_index.impacts | 26 |
| abstract_inverted_index.medical | 8, 34, 62, 102, 109 |
| abstract_inverted_index.patient | 115 |
| abstract_inverted_index.records | 98 |
| abstract_inverted_index.success | 20 |
| abstract_inverted_index.synergy | 30 |
| abstract_inverted_index.without | 163 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.assuming | 38 |
| abstract_inverted_index.database | 73, 77 |
| abstract_inverted_index.dominant | 40 |
| abstract_inverted_index.dynamics | 45 |
| abstract_inverted_index.includes | 147 |
| abstract_inverted_index.inferred | 133 |
| abstract_inverted_index.studying | 85 |
| abstract_inverted_index.comprises | 124 |
| abstract_inverted_index.decision, | 131 |
| abstract_inverted_index.decisions | 160 |
| abstract_inverted_index.diagnosis | 97, 130, 159 |
| abstract_inverted_index.emergency | 103 |
| abstract_inverted_index.intricate | 44 |
| abstract_inverted_index.potential | 13 |
| abstract_inverted_index.practice. | 63 |
| abstract_inverted_index.presently | 138 |
| abstract_inverted_index.property, | 127 |
| abstract_inverted_index.sequence, | 153 |
| abstract_inverted_index.AI-related | 122 |
| abstract_inverted_index.Artificial | 0 |
| abstract_inverted_index.clinicians | 37, 51 |
| abstract_inverted_index.databases, | 120 |
| abstract_inverted_index.healthcare | 16 |
| abstract_inverted_index.practices. | 17 |
| abstract_inverted_index.translated | 60 |
| abstract_inverted_index.treatment. | 168 |
| abstract_inverted_index.assistance, | 165 |
| abstract_inverted_index.clinicians. | 91 |
| abstract_inverted_index.examination | 150 |
| abstract_inverted_index.information | 123, 146 |
| abstract_inverted_index.preliminary | 156 |
| abstract_inverted_index.recommended | 167 |
| abstract_inverted_index.well-chosen | 117 |
| abstract_inverted_index.Intelligence | 1 |
| abstract_inverted_index.interactions | 47, 87 |
| abstract_inverted_index.specialists, | 35 |
| abstract_inverted_index.undiscovered | 53 |
| abstract_inverted_index.collaborative | 96 |
| abstract_inverted_index.probabilities | 134 |
| abstract_inverted_index.revolutionize | 15 |
| abstract_inverted_index.Unfortunately, | 42 |
| abstract_inverted_index.groundbreaking | 72 |
| abstract_inverted_index.AI.vs.Clinician. | 75 |
| abstract_inverted_index.life-threatening | 101 |
| abstract_inverted_index.clinician-related | 145 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 24 |
| citation_normalized_percentile |