ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2024
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis.
Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled it
- Participatory Design of AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- The Cognitive Transaction: Toward a Human Factors Research Agenda for AI in Anesthesia and Perioperative Care.JMIR human factors · 2026Article
- How Can Clinicians Decide if AI-Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?Journal of evaluation in clinical practice · 2026Article
- Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care.Journal of medical Internet research · 2026Article
- The Role of Explanations in AI-Generated Alerts: Qualitative Study of Clinical Views on Explainable AI in Predictive Tools.JMIR human factors · 2026Article
- ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.NPJ digital medicine · 2026Article
- Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study.JMIR biomedical engineering · 2026Article
- Exploring Collaboration Breakdowns Between Provider Teams and Patients in Post-Surgery Care.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- Article
- A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence for Predicting Difficult Airways: A Review.Journal of clinical medicine · 2025Review
- Research Progress on Sepsis Diagnosis and Monitoring Based on Omics Technologies: A Review.Diagnostics (Basel, Switzerland) · 2025Review
- To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events.Proceedings of the ACM on human-computer interaction · 2025Article
- ClinSegNet: Towards Reliable and Enhanced Histopathology Screening.Bioengineering (Basel, Switzerland) · 2025Article
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Improving Sepsis Prediction in the ICU with Explainable Artificial Intelligence: The Promise of Bayesian Networks.Journal of clinical medicine · 2025Article
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.