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Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making

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arxiv 2405.16424 v1 pith:5DXY5SFD submitted 2024-05-26 cs.HC cs.AIcs.LG

Min Hun Lee , Silvana Xin Yi Choo , Shamala D/O Thilarajah This is my paper · ORCID
classification cs.HCcs.AIcs.LG
keywords human-aionboardingcollaborativefindingshealthperformancedecision-makingimproving
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With advanced AI/ML, there has been growing research on explainable AI (XAI) and studies on how humans interact with AI and XAI for effective human-AI collaborative decision-making. However, we still have a lack of understanding of how AI systems and XAI should be first presented to users without technical backgrounds. In this paper, we present the findings of semi-structured interviews with health professionals (n=12) and students (n=4) majoring in medicine and health to study how to improve onboarding with AI and XAI. For the interviews, we built upon human-AI interaction guidelines to create onboarding materials of an AI system for stroke rehabilitation assessment and AI explanations and introduce them to the participants. Our findings reveal that beyond presenting traditional performance metrics on AI, participants desired benchmark information, the practical benefits of AI, and interaction trials to better contextualize AI performance, and refine the objectives and performance of AI. Based on these findings, we highlight directions for improving onboarding with AI and XAI and human-AI collaborative decision-making.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview

    cs.HC 2026-04 conditional novelty 6.0 of 10

    Rule-guided mismatch cues raise Human+AI rehab-assessment accuracy by 14% and cut harmful reliance; prospective embedding previews raise local model-edit gains from 11.5% to 36%, with global transfer often regressing.

  2. Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making

    cs.HC 2025-05 conditional novelty 5.0 of 10

    Distance-based uncertainty visualizations with interactive examples improved correct decisions by 8.20 percentage points over numeric confidence scores in a 27-participant stroke rehabilitation study.

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