# "Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment arXiv: https://arxiv.org/abs/2101.01524 ยท HTML: https://mlsanity.com/paper/2101.01524/ ## Key facts - Cited by 28 papers in the map (data through 14 Oct 2026). - Builds on 7 of its 104 references that are in the map. - First posted on arXiv 4 Jan 2021. ## Builds on (most cited first, 7 of 7) - [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016) - [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019) - [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019) - [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (2019) - [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020) - [Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop](https://mlsanity.com/paper/1703.08428/) (2017) - [AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates](https://mlsanity.com/paper/1912.06723/) (2019) ## Built on by (most cited first, 28 of 28) - [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023) - [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023) - [Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables](https://mlsanity.com/paper/2207.13834/) (2022) - [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023) - [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022) - [Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks](https://mlsanity.com/paper/2402.05650/) (2024) - [Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making](https://mlsanity.com/paper/2302.08807/) (2023) - [Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnostics](https://mlsanity.com/paper/2410.02026/) (2024) - [CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients](https://mlsanity.com/paper/2402.04620/) (2024) - [Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks](https://mlsanity.com/paper/2502.10526/) (2025) - [Current State of Community-Driven Radiological AI Deployment in Medical Imaging](https://mlsanity.com/paper/2212.14177/) (2022) - [A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples](https://mlsanity.com/paper/2404.15974/) (2024) - [Factors that influence the adoption of human-AI collaboration in clinical decision-making](https://mlsanity.com/paper/2204.09082/) (2022) - [Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making](https://mlsanity.com/paper/2505.18066/) (2025) - [Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making](https://mlsanity.com/paper/2405.16424/) (2024) - [Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study](https://mlsanity.com/paper/2510.23578/) (2025) - [L2CU: Learning to Complement Unseen Users](https://mlsanity.com/paper/2601.06119/) (2026) - ["It depends": Configuring AI to Improve Clinical Usefulness Across Contexts](https://mlsanity.com/paper/2407.11978/) (2024) - [Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective](https://mlsanity.com/paper/2504.10918/) (2025) - [Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics](https://mlsanity.com/paper/2602.00726/) (2026) - [From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making](https://mlsanity.com/paper/2603.18895/) (2026) - [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022) - [Toward a Human-Centered AI-assisted Colonoscopy System in Australia](https://mlsanity.com/paper/2503.20790/) (2025) - [Within the MDT Room: Situated in Multidisciplinary Team-Grounded Agent Debate for Clinical Diagnosis](https://mlsanity.com/paper/2603.28393/) (2026) - [AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness](https://mlsanity.com/paper/2605.28680/) (2026) - [RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview](https://mlsanity.com/paper/2606.00011/) (2026) - [Improving Rural Medication Safety with AI: A Scoping Review](https://mlsanity.com/paper/2608.18135/) (2026) - [RecalibrateGPT: AI Fatigue Resilient Conversational Interfaces](https://mlsanity.com/paper/2609.00506/) (2026) ## 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.