CHERIE: User-Centred Development of an XAI System for Chest Radiology through Co-Design

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Header Abstract Content Author and article information REFERENCES Comments

Record: found
Abstract: found
Conference Proceedings: found

Is Open Access

CHERIE: User-Centred Development of an XAI System for Chest Radiology through Co-Design

Published

proceedings-article

Author(s): Shereen Fouad , Lilit Hakobyan , Manolya Kavakli-Thorne , Sarah Atkins , Antonio Fratini , Izegbua E. Ihongbe , Bahadar Bhatia , Arvind Rajasekaran

Publication date (Print): July 2024

Conference name: 37th International BCS Human-Computer Interaction Conference (BCS HCI 24)

Conference theme: The International BCS Human-Computer Interaction Conference in 2024 was supported by the BCS Interactions Special Interest Group and hosted by the University of Central Lancashire in Preston. The BCS HCI Conference welcomed submissions on all aspects of human-computer interaction. Topics included: user experience (UX), usability testing, interaction design (IxD), human-centred AI (HCAI), education, health, sustainability, the Internet of Things (IoT), interaction technologies, and emerging interactive applications.

Conference date: 15–17 July 2024

Keywords: User-centred design, Co-design, Explainable AI, Medical imaging

Bookmark

Abstract

Content

Author and article information

Conference

Publication date: July 2024

Publication date (Print): July 2024

Pages: 205-210

Affiliations

[0001]Applied AI and Robotics

Aston University,UK

[0002]Engineering and Physical Sciences

Aston University, UK

[0003]Aston Digital Futures Institute

Aston University, UK

[0004]Social Sciences and Humanities

Aston University, UK

[0005]John Morlese

Sandwell And West Birmingham Hospitals NHS Trust, UK

Article

DOI: 10.14236/ewic/BCSHCI2024.19

SO-VID: 6eaf1af9-1705-4e65-bb59-2e41ed07fa5b

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 37th International BCS Human-Computer Interaction Conference

Conference acronym: BCS HCI 24

Conference number: 37

Conference location: University of Central Lancashire (UCLan)

Conference date: 15–17 July 2024

Conference sponsor: Electronic Workshops in Computing (eWiC)

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/BCSHCI2024.19

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

ArrietaA.B., D´ıaz-Rodr´ıguezN., Del SerJ., BennetotA., TabikS., BarbadoA., Garc´ıaS., Gil-L´opezS., MolinaD., BenjaminsR. and ChatilaR. (2020) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, pp.82–115.
BarzasK., FouadS., JasaG., and LandiniG., (2023) An Explainable Deep Learning Framework for Mandibular Canal Segmentation from Cone Beam Computed Tomography Volumes. The 12th International Conference on Computational Advances in Bio and Medical Sciences. Springer.
FouadS., LandiniG., RobinsonM., SongT.-H., and MehannaH. (2021) Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks. Computerized Medical Imaging and Graphics, 88, 101853.
FouadS., RandellD., GaltonA., MehannaH., and LandiniG. (2017) Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixelbased segmentation. Journal of Imaging, 3(4), 61.
GerlingsJ., JensenM.S. and SholloA. (2022) Explainable AI, but explainable to whom? An exploratory case study of xAI in healthcare. Handbook of Artificial Intelligence in Healthcare: Vol 2: Practicalities and Prospects, pp.169–198.
IhongbeI., FouadS., MahmoudT., RajasekaranA., and BhatiaB., (2024) Evaluating Explainable Artificial Intelligence (XAI) Techniques in Chest Radiology Imaging Through a Human-Centered Lens. Submitted for publication in PLOS ONE.
LimC.-P., VaidyaA., JainK., MahorkarV. U., and JainL.C., (2022) Handbook of Artificial Intelligence in Healthcare. Springer.
NeuhauserL., KrepsG. L., MorrisonK., AthanasoulisM., KirienkoN. and Van BruntD. (2013) Using design science and artificial intelligence to improve health communication: ChronologyMD case example. Patient education and counseling, 92(2), 211–217.
NyariroM., EmamiE., CaidorP. and RahimiS.A. (2023) Integrating equity, diversity and inclusion throughout the lifecycle of AI within healthcare: a scoping review protocol. BMJ open, 13(9), e072069.
PaniguttiC., BerettaA., FaddaD., GiannottiF., PedreschiD., PerottiA. and RinzivilloS. (2023) Co-design of human-centered, explainable AI for clinical decision support. ACM Transactions on Interactive Intelligent Systems, 13(4), pp.1–35.
RaparthiM. (2020) AI Integration in Precision Health-Advancements, Challenges, and Future Prospects. Asian Journal of Multidisciplinary Research and Review, 1(1), 90–96.
RibeiroM. T., SinghS., and GuestrinC., (2016) ”Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
SchoonderwoerdT. A., JorritsmaW., NeerincxM. A. and Van Den BoschK. (2021) Humancentered XAI: Developing design patterns for explanations of clinical decision support systems. International Journal of Human-Computer Studies, 154, 102684.
SelvarajuR. R., CogswellM., DasA., VedantamR., ParikhD., and BatraD., (2017) Gradcam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, 618–626.
ShulhaM., HovdeboJ., D’SouzaV., ThibaultF. and HarmoucheR. (2024) Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach. JMIR Formative Research, 8(1), p.e50475.
TjoaE. and GuanC. (2020) A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems, 32(11), 4793–4813.
de VriesB.M., ZwezerijnenG. J. C., BurchellG. L., van VeldenF.H.P., Menke-van der Houven van OordtC.W., and BoellaardR. (2023) Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Frontiers in Medicine, 10, 1180773.
ZhukovskaA., ZheliukT., ShushpanovD., BrychV., BrechkoO., and KryvokulskaN. (2023) Management of the Development of Artificial Intelligence in Healthcare. Proceedings of the 13th International Conference on Advanced Computer Information Technologies (ACIT), 241–247.
ZicariR.V., AhmedS., AmannJ., BraunS.A., BrodersenJ., BruneaultF., BrusseauJ., CampanoE., CoffeeM., DengelA. and D¨udderB. (2021) Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier. Frontiers in Human Dynamics, 3, p.688152.

Comments

Comment on this article

[1] ArrietaA.B., D´ıaz-Rodr´ıguezN., Del SerJ., BennetotA., TabikS., BarbadoA., Garc´ıaS., Gil-L´opezS., MolinaD., BenjaminsR. and ChatilaR. (2020) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, pp.82–115.

[2] BarzasK., FouadS., JasaG., and LandiniG., (2023) An Explainable Deep Learning Framework for Mandibular Canal Segmentation from Cone Beam Computed Tomography Volumes. The 12th International Conference on Computational Advances in Bio and Medical Sciences. Springer.

[3] FouadS., LandiniG., RobinsonM., SongT.-H., and MehannaH. (2021) Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks. Computerized Medical Imaging and Graphics, 88, 101853.

[4] FouadS., RandellD., GaltonA., MehannaH., and LandiniG. (2017) Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixelbased segmentation. Journal of Imaging, 3(4), 61.

[5] GerlingsJ., JensenM.S. and SholloA. (2022) Explainable AI, but explainable to whom? An exploratory case study of xAI in healthcare. Handbook of Artificial Intelligence in Healthcare: Vol 2: Practicalities and Prospects, pp.169–198.

[6] IhongbeI., FouadS., MahmoudT., RajasekaranA., and BhatiaB., (2024) Evaluating Explainable Artificial Intelligence (XAI) Techniques in Chest Radiology Imaging Through a Human-Centered Lens. Submitted for publication in PLOS ONE.

[7] LimC.-P., VaidyaA., JainK., MahorkarV. U., and JainL.C., (2022) Handbook of Artificial Intelligence in Healthcare. Springer.

[8] NeuhauserL., KrepsG. L., MorrisonK., AthanasoulisM., KirienkoN. and Van BruntD. (2013) Using design science and artificial intelligence to improve health communication: ChronologyMD case example. Patient education and counseling, 92(2), 211–217.

[9] NyariroM., EmamiE., CaidorP. and RahimiS.A. (2023) Integrating equity, diversity and inclusion throughout the lifecycle of AI within healthcare: a scoping review protocol. BMJ open, 13(9), e072069.

[10] PaniguttiC., BerettaA., FaddaD., GiannottiF., PedreschiD., PerottiA. and RinzivilloS. (2023) Co-design of human-centered, explainable AI for clinical decision support. ACM Transactions on Interactive Intelligent Systems, 13(4), pp.1–35.

[11] RaparthiM. (2020) AI Integration in Precision Health-Advancements, Challenges, and Future Prospects. Asian Journal of Multidisciplinary Research and Review, 1(1), 90–96.

[12] RibeiroM. T., SinghS., and GuestrinC., (2016) ”Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.

[13] SchoonderwoerdT. A., JorritsmaW., NeerincxM. A. and Van Den BoschK. (2021) Humancentered XAI: Developing design patterns for explanations of clinical decision support systems. International Journal of Human-Computer Studies, 154, 102684.

[14] SelvarajuR. R., CogswellM., DasA., VedantamR., ParikhD., and BatraD., (2017) Gradcam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, 618–626.

[15] ShulhaM., HovdeboJ., D’SouzaV., ThibaultF. and HarmoucheR. (2024) Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach. JMIR Formative Research, 8(1), p.e50475.

[16] TjoaE. and GuanC. (2020) A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems, 32(11), 4793–4813.

[17] de VriesB.M., ZwezerijnenG. J. C., BurchellG. L., van VeldenF.H.P., Menke-van der Houven van OordtC.W., and BoellaardR. (2023) Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Frontiers in Medicine, 10, 1180773.

[18] ZhukovskaA., ZheliukT., ShushpanovD., BrychV., BrechkoO., and KryvokulskaN. (2023) Management of the Development of Artificial Intelligence in Healthcare. Proceedings of the 13th International Conference on Advanced Computer Information Technologies (ACIT), 241–247.

[19] ZicariR.V., AhmedS., AmannJ., BraunS.A., BrodersenJ., BruneaultF., BrusseauJ., CampanoE., CoffeeM., DengelA. and D¨udderB. (2021) Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier. Frontiers in Human Dynamics, 3, p.688152.

UK Computing Summit 2025: Navigating change (surviving and beyond) - 29-30 April @ Sheffield Hallam University - Register here.

CHERIE: User-Centred Development of an XAI System for Chest Radiology through Co-Design

Conference

Affiliations

Article

History

Product

Categories

Comment on this article