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- Convenors:
-
olivier ocquidant
(Télécom Paris)
Sylvie Grosjean (University of Ottawa)
Rob Procter (Warwick University Alan Turing Institute for Data Science and AI)
Gérald Gaglio (University Côte d'Azur)
christian licoppe (Telecom Paris and I3 ( CNRS UMR 9217))
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- Format:
- Traditional Open Panel
Short Abstract
The panel focuses on how medical practice is reframed through the use of decision-support AI technologies. From an STS perspective, it highlights the need for detailed understandings of the technologies based on their practices, and for empirical studies of medical activity.
Description
Beyond the often alarmist narratives surrounding the impact of decision-support AI technologies on medical work, there remains a lack of precise knowledge about what concrete changes these tools bring to medical or clinical decision-making, and what adjustments and re-configurations they entail in physicians’ work.
Existing studies identified ethical and trust questions, epistemic tensions with trainers, and healthcare organizational reconfiguration concerns. Yet, the specific effects of decision-support AI tools on the real work of physicians (radiologists, anatomic pathologists, surgeons, dermatologists, psychiatrists, etc.) remain insufficiently explored. Pioneering research in radiology has shown, for instance, that computer-aided diagnostic tools often add to radiologists’ workload. Other studies suggest that AI may increase practitioners’ reflexivity about their decisions, while still others highlight how these tools reshape sensemaking practices in medical work. Despite their different angles, these works converge on a shared concern around adjustments and arrangements made by the practitioners when using these new tools.
This panel invites contributions covering diverse examples of medical decision-making practices that illuminate how physicians’ activities are rearranged and reframed when assisted by AI decision support tools:
• What kinds of changes occur in their work—procedurally or experientially?
• Which kinds of tasks – individual or organizational – are specifically reframed or induced by these new tools and how?
• What modes of use are observed, and how do these vary between different professional contexts?
• How is physicians’ trust in the performance of AI decision support tools calibrated and sustained in use?
We welcome empirical studies of AI in medical decision-making, including from STS and social science of medicine that apply ethnographic and micro-analytic approaches (e.g. ethnomethodology, video analysis, human factors, distributed cognition). Contributions from human–machine interaction perspectives are also encouraged.