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Paper short abstract

We show how radiology educators and students practically make AI-based tools teachable, revealing how software interface structures learning and how radiomics pedagogy emerges through users’ situated, embodied work with the platform.

Paper long abstract

This paper examines how medical professionals, educators, and students practically engage with AI‑based tools in radiology, focusing on the sequential and embodied work through which such technologies are made accountable, intelligible, and teachable [1]. Drawing on EMCA‑informed video‑ethnographic studies of teaching sessions using the radiomics platform QuantImage [2], we show how participants orient to the software not only as a computational instrument but as an interactional partner whose interface and constraints must be navigated and incorporated into pedagogical work.

Radiomics – where quantitative features are extracted from imaging data and processed through machine‑learning models – poses persistent challenges of interpretability and trust [3]. While technical advances are substantial, little is known about how clinicians and trainees actually work with radiomic models in situ or learn to integrate them into medical workflows. Our study investigates how educators and students co‑construct learning environments around QuantImage, and how its design embodies a formalized version of the radiomics “workflow” that participants must learn to inhabit.

A central focus is the coherence between the “introductory” and “practical” parts of radiomics teaching. We show how instructors prospectively frame what will matter in the hands‑on session, and how participants retrospectively mobilize earlier explanations while working through the software’s stepwise procedures. These practices accomplish the recognizability of the activity as teaching and learning radiomics. We argue that learning AI‑based tools involves aligning with the praxeological analysis embedded in the software itself, respecifying AI as an ongoing accomplishment of human‑with‑machine practices.

[1] https://doi.org/10.3389/fcomm.2023.1234987

[2] https://doi.org/10.1186/s41747-023-00326-z

[3] https://doi.org/10.1002/med.21846