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

arXiv: https://arxiv.org/abs/2402.13437 · HTML: https://mlsanity.com/paper/2402.13437/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks](https://mlsanity.com/paper/2502.10526/) (2025)
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- [AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages](https://mlsanity.com/paper/2505.10300/) (2025)
- [Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitioners](https://mlsanity.com/paper/2602.11342/) (2026)
- [Exploring the Innovation Opportunities for Pre-trained Models](https://mlsanity.com/paper/2505.15790/) (2025)
- [Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions](https://mlsanity.com/paper/2602.00259/) (2026)

## Abstract (arXiv)

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.