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In this study, we present a human-centered approach to the problem and describe insights derived following contextual inquiry and in-depth interviews with 15 clinical stakeholders. The interviews helped understand challenges in existing workflows, and how best to align technical capabilities with user needs and expectations. We discovered the tradeoffs and complexities that need consideration when choosing suitable workflow stages, target users, and design configurations for different AI proposals. We explored how to balance AI benefits and risks for healthcare staff and patients within broader organizational, technical, and medical-legal constraints. We also identified data issues related to edge cases and data biases that affect model training and evaluation; how data documentation practices influence data preparation and labeling; and how to measure relevant AI outcomes reliably in future evaluations. We discuss how our work informs design and development of AI applications that are clinically useful, ethical, and acceptable in real-world healthcare services.<\/jats:p>","DOI":"10.1145\/3716500","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T12:13:02Z","timestamp":1739362382000},"page":"1-61","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding\u00a0Tube Qualification in Radiology"],"prefix":"10.1145","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9639-5531","authenticated-orcid":false,"given":"Anja","family":"Thieme","sequence":"first","affiliation":[{"name":"Health Futures, Microsoft Research, Cambridge, United Kingdom of Great Britain and Northern 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