Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology

• Published in: ACM transactions on computer-human interaction Vol. 32; no. 4; pp. 1 - 61
• Main Authors: Thieme, Anja, Rajamohan, Abhijith, Cooper, Benjamin, Groombridge, Heather, Simister, Robert, Wong, Barney, Woznitza, Nicholas, Pinnock, Mark A., Wetscherek, Maria T., Morrison, Cecily, Richardson, Hannah, Pérez-García, Fernando, Hyland, Stephanie L., Bannur, Shruthi, Coelho de Castro, Daniel, Bouzid, Kenza, Schwaighofer, Anton, Ranjit, Mercy P., Sharma, Harshita, Lungren, Matthew P., Oktay, Ozan, Alvarez-Valle, Javier, Nori, Aditya, Harris, Steve, Jacob, Joseph
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 19.08.2025
• Subjects: Computing methodologies; Empirical studies in HCI; Human-centered computing; Machine learning algorithms; responsible AI; Radiology; AI; NGT; feeding tubes; healthcare; socio-technical systems
• ISSN: 1073-0516; 1557-7325
• DOI: 10.1145/3716500

Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images to reduce risks of sub-optimally or critically placed NGTs being missed or delayed in their detection, but gaps remain in clinical practice integration. 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.