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

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

TL;DR

This database is the first of its kind for studying the interactions between AI and clinicians and derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency -- Sepsis -- from 14 medical centers across China.

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.

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

TL;DR

This database is the first of its kind for studying the interactions between AI and clinicians and derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency -- Sepsis -- from 14 medical centers across China.

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.
Paper Structure (16 sections, 2 figures, 3 tables)

This paper contains 16 sections, 2 figures, 3 tables.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Data Records
  4. Technical Validation
  5. Usage Notes
  6. Code availability
  7. Acknowledgements
  8. Author contributions statement
  9. Competing interests

Figures (2)

  • Figure 1: AI.vs.Clinician Database Development Process. The human-AI interaction data are acquired from the clinical trials using AI models in 14 medical centers. A patient cohort is created based on the MIMIC databases johnson2016mimicjohnson2020mimicjohnson2019mimicjohnson2023mimic and used for the collaborative decision-making of AI models and clinicians. Finally, the collected data are validated and de-identified. Tables in AI.vs.Clinician are classified into three categories: patient cohort that records the patients' information and examination data; AI models that record the AI model related information and inference results on patient cohort, and clinicians that record the clinician information and interaction behaviors with AI models.
  • Figure 2: Patient Cohort Creation.