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Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
,
Chiara Natali,
Matt Roach ,
Darren Scott,
Alma Rahat ,
David Rawlinson,
Federico Cabitza
Computer Supported Cooperative Work (CSCW)
Swansea University Authors:
Ben Wilson , Matt Roach , Darren Scott, Alma Rahat
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DOI (Published version): 10.1007/s10606-025-09514-4
Abstract
Whilst it is commonly reported that healthcare is set to benefit from advances in Artificial Intelligence (AI), there is a consensus that, for clinical AI, a gulf exists between conception and implementation. Here we advocate the increased use of situated design and evaluation to close this gap, sho...
| Published in: | Computer Supported Cooperative Work (CSCW) |
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| ISSN: | 0925-9724 1573-7551 |
| Published: |
Springer Science and Business Media LLC
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69051 |
| Abstract: |
Whilst it is commonly reported that healthcare is set to benefit from advances in Artificial Intelligence (AI), there is a consensus that, for clinical AI, a gulf exists between conception and implementation. Here we advocate the increased use of situated design and evaluation to close this gap, showing that in the literature there are comparatively few prospective situated studies. Focusing on the combined human-machine decision-making process - modelling, exchanging and resolving - we highlight the need for advances in exchanging and resolving. We present a novel relational space - contextual dimensions of combination - a means by which researchers, developers and clinicians can begin to frame the issues that must be addressed in order to close the chasm. We introduce a space of eight initial dimensions, namely participating agents, control relations, task overlap, temporal patterning, informational proximity, informational overlap, input influence and output representation coverage. We propose that our awareness of where we are in this space of combination will drive the development of interactions and the designs of AI models themselves. Designs that take account of how user-centered they will need to be for their performance to be translated into societal and individual benefit. |
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| Keywords: |
Human-AI interaction, Human-centered AI, Hybrid intelligence, Real-world evaluation, Medical AI |
| College: |
Faculty of Science and Engineering |
| Funders: |
Open access funding provided by Università degli Studi di Milano - Bicocca within the CRUI-CARE Agreement. B. Wilson gratefully acknowledges that this work was supported by the UK Engineering and Physical Sciences Research Council grant EP/S021892/1 and funding from EMRTS, Cymru. B. Wilson also gratefully acknowledges funding for this work by the European Union.
C. Natali gratefully acknowledges the PhD grant awarded by the Fondazione Fratelli Confalonieri, which has been instrumental in facilitating her research pursuits. C. Natali also gratefully acknowledges the financial support provided by the Federal Commission for Scholarships for Foreign Students in the form of the Swiss Government Excellence Scholarship (ESKAS No. 2024.0002) for the academic year 2024-25.
F. Cabitza acknowledges funding support provided by the Italian project PRIN PNRR 2022 InXAID - Interaction with eXplainable Artificial Intelligence in (medical) Decision making. CUP: H53D23008090001 funded by the European Union - Next Generation EU.
M. Roach acknowledges funding for this work by the European Union. |