From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
Journal:
arXiv
Published Date:
Jun 16, 2025
Abstract
In this work, we reflect on the data-driven modeling paradigm that is gaining
ground in AI-driven automation of patient care. We argue that the repurposing
of existing real-world patient datasets for machine learning may not always
represent an optimal approach to model development as it could lead to
undesirable outcomes in patient care. We reflect on the history of data
analysis to explain how the data-driven paradigm rose to popularity, and we
envision ways in which systems thinking and clinical domain theory could
complement the existing model development approaches in reaching human-centric
outcomes. We call for a purpose-driven machine learning paradigm that is
grounded in clinical theory and the sociotechnical realities of real-world
operational contexts. We argue that understanding the utility of existing
patient datasets requires looking in two directions: upstream towards the data
generation, and downstream towards the automation objectives. This
purpose-driven perspective to AI system development opens up new methodological
opportunities and holds promise for AI automation of patient care.
