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Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit.

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Summary
This summary is machine-generated.

Multidisciplinary teams can improve artificial intelligence (AI) innovation by using design workshops. This approach helps identify buildable and desirable AI use cases, particularly in critical care medicine.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Critical Care Medicine

Background:

  • Innovation teams struggle to envision desirable and buildable artificial intelligence (AI) concepts.
  • A disconnect exists between data science (unwanted innovations) and domain experts (unbuildable innovations).
  • Effective ideation is a critical bottleneck in AI development.

Purpose of the Study:

  • To explore effective approaches for AI concept ideation and problem formulation.
  • To present a firsthand account of ideating AI concepts for critical care medicine.
  • To identify methods for multidisciplinary teams to generate buildable and desirable AI use cases.

Main Methods:

  • Conducted a series of design workshops with a multidisciplinary team (data scientists, clinicians, HCI researchers).
  • Explored effective approaches to AI concept ideation and problem formulation.
  • Documented the process, challenges, and effective practices and artifacts.

Main Results:

  • Identified challenges in AI concept ideation across different expert groups.
  • Developed and tested practices and artifacts to facilitate effective AI ideation.
  • Demonstrated a structured approach to bridge the gap between technical feasibility and user desirability.

Conclusions:

  • Effective AI ideation requires structured collaboration between diverse stakeholders.
  • Human-Computer Interaction (HCI) can play a crucial role in reducing AI innovation failure rates.
  • The presented methods offer a pathway for improved stakeholder engagement and AI development in critical care medicine.