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AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database

11 June 2024
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
Peng Pan
Wei Huang
Zhongmou Liu
Jizhong Hu
Fan Huang
Gangyuan Zhao
Chongrong Jiang
Tianyi Wei
Zhifei Zhang
Yunyou Huang
Jianfeng Zhan
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Abstract

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.

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