Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care
Year: 2024
Type: preprint
Abstract: There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustworthiness, impacting safe and effective adoption of such technologies. Improved understanding of decision-making processes and requirements for explanations coming from decision support tools is a vital component in providing effective explainable solutions. This is particularly relevant in the data-intensive, fast-paced environments of intensive care units (ICUs). To explore these issues, group interviews were conducted with seven ICU clinicians, representing various roles and experience levels. Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.
Source: arXiv (Cornell University)
Institution: University of Bristol
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: SMARTT Critical Care Pathways - (Safe, Machine Assisted, Real Time Transfer) An artificial intelligence based decision support tool to enable safer and more timely critical care transfer, LEAP (Leadership Engagement Acceleration & Partnership) - an EPSRC Digital Health Hub, Turing AI Fellowship: Interactive Annotations in AI, G4685247445, G7935266708
OpenAlex ID: W4404571237