Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts

Wilson, Ben; Natali, Chiara; Roach, Matt; Scott, Darren; Rahat, Alma; Rawlinson, David; Cabitza, Federico

doi:10.1007/s10606-025-09514-4

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Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts

Ben Wilson

, 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...

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Published in:	Computer Supported Cooperative Work (CSCW)
ISSN:	0925-9724 1573-7551
Published:	Springer Science and Business Media LLC 2025
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa69051

first_indexed	2025-03-07T05:49:40Z
last_indexed	2025-08-01T14:30:57Z
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spelling	2025-07-31T14:58:06.8657088 v2 69051 2025-03-06 Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts a854728f3952ca0b74a49f9286a9b0e2 0009-0004-5663-5854 Ben Wilson Ben Wilson true false 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false b824c162f7755b171e29e8289d1a0ac8 Darren Scott Darren Scott true false 6206f027aca1e3a5ff6b8cd224248bc2 0000-0002-5023-1371 Alma Rahat Alma Rahat true false 2025-03-06 MACS 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. Journal Article Computer Supported Cooperative Work (CSCW) 0 Springer Science and Business Media LLC 0925-9724 1573-7551 Human-AI interaction, Human-centered AI, Hybrid intelligence, Real-world evaluation, Medical AI 14 4 2025 2025-04-14 10.1007/s10606-025-09514-4 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee 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. 2025-07-31T14:58:06.8657088 2025-03-06T14:07:05.0240579 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ben Wilson 0009-0004-5663-5854 1 Chiara Natali 2 Matt Roach 0000-0002-1486-5537 3 Darren Scott 4 Alma Rahat 0000-0002-5023-1371 5 David Rawlinson 6 Federico Cabitza 7 69051__34069__f5a33ede0b2b4ee89a47d423f0ac0ef0.pdf 69051.VoR.pdf 2025-04-23T15:14:25.7484362 Output 1749469 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
spellingShingle	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts Ben Wilson Matt Roach Darren Scott Alma Rahat
title_short	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
title_full	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
title_fullStr	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
title_full_unstemmed	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
title_sort	Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts
author_id_str_mv	a854728f3952ca0b74a49f9286a9b0e2 9722c301d5bbdc96e967cdc629290fec b824c162f7755b171e29e8289d1a0ac8 6206f027aca1e3a5ff6b8cd224248bc2
author_id_fullname_str_mv	a854728f3952ca0b74a49f9286a9b0e2_*_Ben Wilson 9722c301d5bbdc96e967cdc629290fec__Matt Roach b824c162f7755b171e29e8289d1a0ac8__Darren Scott 6206f027aca1e3a5ff6b8cd224248bc2_*_Alma Rahat
author	Ben Wilson Matt Roach Darren Scott Alma Rahat
author2	Ben Wilson Chiara Natali Matt Roach Darren Scott Alma Rahat David Rawlinson Federico Cabitza
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publishDate	2025
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publisher	Springer Science and Business Media LLC
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department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description	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.
published_date	2025-04-14T05:27:10Z
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Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts

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