AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911970693742592 |
|---|---|
| author | Gao, Wanling Liu, Yuan Yu, Zhuoming Cui, Dandan Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Huang, Fan Zhao, Gangyuan Jiang, Chongrong Wei, Tianyi Zhang, Zhifei Huang, Yunyou Zhan, Jianfeng |
| author_facet | Gao, Wanling Liu, Yuan Yu, Zhuoming Cui, Dandan Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Huang, Fan Zhao, Gangyuan Jiang, Chongrong Wei, Tianyi Zhang, Zhifei Huang, Yunyou Zhan, Jianfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07362 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database Gao, Wanling Liu, Yuan Yu, Zhuoming Cui, Dandan Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Huang, Fan Zhao, Gangyuan Jiang, Chongrong Wei, Tianyi Zhang, Zhifei Huang, Yunyou Zhan, Jianfeng Human-Computer Interaction 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. |
| title | AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.07362 |