mlsanityData to 7 Oct 2026

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

arXiv 2101.01524 ↗First posted 4 Jan 2021cs.HC cs.AI cs.LGcited by 28

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Abstract (arXiv)

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 7 of its 104 references that are in the map. First posted on arXiv 4 Jan 2021.

Builds on

7 of 104 references.

References · 7

  1. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  2. 2019Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Makingcited by 109
  3. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  4. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  5. 2020Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systemscited by 36
  6. 2017Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loopcited by 26
  7. 2019AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinatescited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 4; 2024: 6; 2025: 5; 2026: 8.

28 papers.

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  1. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  2. 2023Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosiscited by 30
  3. 2022Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservablescited by 19
  4. 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
  5. 2022Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shiftscited by 16
  6. 2024Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Taskscited by 13
  7. 2023Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Makingcited by 11
  8. 2024Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnosticscited by 11
  9. 2024CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patientscited by 9
  10. 2025Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Taskscited by 6
  11. 2022Current State of Community-Driven Radiological AI Deployment in Medical Imagingcited by 5
  12. 2024A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examplescited by 5
16 more
  1. 2022Factors that influence the adoption of human-AI collaboration in clinical decision-makingcited by 4
  2. 2025Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Makingcited by 4
  3. 2024Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Makingcited by 2
  4. 2025Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Studycited by 2
  5. 2026L2CU: Learning to Complement Unseen Userscited by 2
  6. 2024"It depends": Configuring AI to Improve Clinical Usefulness Across Contextscited by 1
  7. 2025Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspectivecited by 1
  8. 2026Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetricscited by 1
  9. 2022Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networkscited by 0
  10. 2025Toward a Human-Centered AI-assisted Colonoscopy System in Australiacited by 0
  11. 2026From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Makingcited by 0
  12. 2026Within the MDT Room: Situated in Multidisciplinary Team-Grounded Agent Debate for Clinical Diagnosiscited by 0
  13. 2026AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulnesscited by 0
  14. 2026RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Previewcited by 0
  15. 2026Improving Rural Medication Safety with AI: A Scoping Reviewcited by 0
  16. 2026RecalibrateGPT: AI Fatigue Resilient Conversational Interfacescited by 0