Generative AI Applications in AKI and Critical Care
Abstract List
20 Sep 2026
14:00
15:30
Room 105
Learning Objective
By the end of this case-based learning session, participants will be able to apply the IMPACT framework to systematically evaluate the appropriateness, reliability, and potential risks of generative AI tools in ICU clinical contexts, while using AI-assisted workflows for reference management and graphical abstract creation to enhance research communication and citation potential.
中文學習目標
完成本案例教學後，參與者將能夠運用 IMPACT framework 於 ICU 臨床情境中系統性評估生成式 AI 工具的適切性、可信度與潛在風險，並實作 AI 輔助的文獻引用管理與 graphical abstract 生成流程，以提升研究溝通效率與論文引用潛力。
| Time | Session |
|---|---|
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14:00
14:45
|
Yu-Chang YEHTaiwan
Speaker
Clinical Support Information with Generative AI: Content Generation and Evaluation Generative artificial intelligence is shifting from single-question answering toward structured clinical decision support at the bedside. This lecture presents a practical approach to generating and evaluating AI-derived clinical support information in critical care. On the generation side, we describe a multi-turn conversation and multi-task workflow in which structured patient data, a machine learning mortality prediction model, and SHAP-based explanations are passed sequentially to a large language model through five linked task prompts covering risk interpretation, syndrome identification, current status and diagnoses, recommended examinations, and management suggestions. Each turn inherits the context of the previous one, so the output accumulates into a coherent clinical narrative rather than a set of isolated answers. On the evaluation side, we introduce the IMPACT Framework, a six-domain, 21-item instrument developed through a multinational Delphi consensus involving 58 panelists from 12 countries. Its domains, Integration, Mastery, Precision, Applicability, Comprehensiveness, and Timeliness, allow both clinicians and automated judges to score generated content reproducibly. We share validation results, examples from an intensive care cohort, and lessons learned from iterative prompt refinement. Attendees will leave with a transferable method for building and auditing generative AI support tools in their own units.
Room 101D
|
|
14:45
15:30
|
Yu-Chen ChuangTaiwan
Speaker
AI-Assisted Reference Management and Graphical Abstract CreationBy the end of this case-based learning session, participants will be able to apply the IMPACT framework to systematically evaluate the appropriateness, reliability, and potential risks of generative AI tools in ICU clinical contexts, while using AI-assisted workflows for reference management and graphical abstract creation to enhance research communication and citation potential.
Room 101D
|