Design Patterns of Human-AI Interfaces in Healthcare

TL;DR

This paper addresses the challenge of translating high-level human-AI interaction principles into concrete healthcare interface designs by conducting a systematic literature review and qualitative validation. It identifies 15 information entities and 12 reusable design patterns, organized into information presentation coordination and interaction design, and grounds them in both literature and semi-structured interviews with healthcare professionals. The approach is evaluated through a designer workshop, revealing that the patterns help ground user needs, expand design exploration, and simplify complex interfaces, while also surfacing unarticulated needs. The work offers a practical, data-driven design language for clinically meaningful human-AI interfaces, with potential generalizability to other domain contexts through abstracted information entities and patterns.

Abstract

Human-AI interfaces play a pivotal role in integrating clinicians' expertise with artificial intelligence to enhance both healthcare practice and research. However, designing effective interfaces in this domain remains a significant challenge. The inherent complexity of medical data, the influence of domain-specific conventions, and the diverse needs of clinical users compound the challenge of developing practical and usable solutions. In this study, we review existing solutions and synthesize a set of design patterns - recurring approaches that support the design of human-AI interfaces in clinical settings. We conducted a comprehensive literature review of human-AI interaction designs in clinical contexts, through which we identified 15 information entities commonly presented to users and 12 design patterns used to organize and communicate this information effectively. For each design pattern, we summarize the underlying design problem, the proposed solution, and the rationale for when the pattern should or should not be applied, based on insights from both the literature and semi-structured interviews with 12 healthcare professionals. We evaluated the proposed design patterns through an online workshop involving 14 experienced UI designers. During the workshop, participants were asked to create interface sketches for healthcare-related scenarios drawn from their own professional experience, using our design patterns as guidance. Our findings show that the proposed design patterns helped participants ground their designs in user needs, generate a wider range of design alternatives, and simplify complex interface structures. We further analyzed and summarized the participants' usage strategies and feedback regarding the applicability and usefulness of the design patterns.

Figures (10)

• Figure 1: The paper collection and coding process.
• Figure 2: The distribution of scenarios in the collected papers.
• Figure 3: We identified design patterns of human-AI interfaces in healthcare in information entity selection, information entity presentation, and interaction design.
• Figure 4: (A) Zhang et al. Zhang2024Rethinking present patient historical risk levels alongside the predicted risks. (B) Jin et al. Jin2020CarePre display a patient's past diseases alongside potential future diseases.
• Figure 5: (A) VBridge Cheng2022Vbridge displays the attribution of each feature, while directly presenting the corresponding values alongside. (B) COVID-view Jadhav2022Covid displays Chest CT images overlaid with feature attributions.