Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence

Mufti Mahmud; David J. Brown; Yuan Shen; Muhammad Arifur Rahman; Jun He; M. Shamim Kaiser; Hamzah Luqman; Sajib Mistry; Noushath Shaffi; Vimbi Viswan; M. Mostafizur Rahman; Shamim Al Mamun; Tamanna Sharmeen; Rasha Alahmad; V. N.Manjunath Aradhya; Mohammad Farukh Hashmi; Shuqiang Wang; Cosimo Ieracitano; Nadia Mammone; Maryam Doborjeh; Kanad Ray

doi:10.1007/978-3-032-13022-8_25

Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence

Mufti Mahmud^*
, David J. Brown
, Yuan Shen
, Muhammad Arifur Rahman
, Jun He
, M. Shamim Kaiser
, Hamzah Luqman
, Sajib Mistry
, Noushath Shaffi
, Vimbi Viswan
, M. Mostafizur Rahman
, Shamim Al Mamun
, Tamanna Sharmeen
, Rasha Alahmad
, V. N.Manjunath Aradhya
, Mohammad Farukh Hashmi
, Shuqiang Wang
, Cosimo Ieracitano
, Nadia Mammone
, Maryam Doborjeh

Kanad Ray

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Artificial Intelligence (AI) and emerging technologies are revolutionising digital human models (DHMs), offering significant opportunities to enhance accessibility and inclusion in healthcare systems. This evolution is further amplified by the concept of the “digital twin”—a virtual representation of a human patient that is dynamically updated with real-world data. This paper explores the potential of explainable AI (XAI) in conjunction with digital twins to create transparent, interpretable, and responsible healthcare solutions, particularly through privacy-preserving techniques like federated learning. By integrating advanced technologies such as computer vision, natural language processing, and machine learning, DHMs can be designed to understand, predict, and simulate the behaviours and requirements of individuals with varying abilities and backgrounds, ultimately creating personalised digital twins for enhanced healthcare.

Original language	English
Title of host publication	HCI International 2025 – Late Breaking Papers - 27th International Conference on Human-Computer Interaction, HCII 2025, Proceedings
Editors	Vincent G. Duffy, Qin Gao, Jia Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	359-374
Number of pages	16
ISBN (Print)	9783032130242
DOIs	https://doi.org/10.1007/978-3-032-13022-8_25
State	Published - 2026
Event	Late breaking papers from the 27th International Conference on Human-Computer Interaction, HCI International 2025 - Gothenburg, Sweden Duration: 22 Jun 2025 → 27 Jun 2025

Publication series

Name	Lecture Notes in Computer Science
Volume	16340 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Late breaking papers from the 27th International Conference on Human-Computer Interaction, HCI International 2025
Country/Territory	Sweden
City	Gothenburg
Period	22/06/25 → 27/06/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Digital Twin
Explainable AI
Federated Learning
Inclusive AI
Preventive Healthcare

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-032-13022-8_25

Cite this

Mahmud, M., Brown, D. J., Shen, Y., Rahman, M. A., He, J., Kaiser, M. S., Luqman, H., Mistry, S., Shaffi, N., Viswan, V., Rahman, M. M., Mamun, S. A., Sharmeen, T., Alahmad, R., Aradhya, V. N. M., Hashmi, M. F., Wang, S., Ieracitano, C., Mammone, N., ... Ray, K. (2026). Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence. In V. G. Duffy, Q. Gao, & J. Zhou (Eds.), HCI International 2025 – Late Breaking Papers - 27th International Conference on Human-Computer Interaction, HCII 2025, Proceedings (pp. 359-374). (Lecture Notes in Computer Science; Vol. 16340 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-13022-8_25

Mahmud, Mufti ; Brown, David J. ; Shen, Yuan et al. / Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence. HCI International 2025 – Late Breaking Papers - 27th International Conference on Human-Computer Interaction, HCII 2025, Proceedings. editor / Vincent G. Duffy ; Qin Gao ; Jia Zhou. Springer Science and Business Media Deutschland GmbH, 2026. pp. 359-374 (Lecture Notes in Computer Science).

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abstract = "Artificial Intelligence (AI) and emerging technologies are revolutionising digital human models (DHMs), offering significant opportunities to enhance accessibility and inclusion in healthcare systems. This evolution is further amplified by the concept of the “digital twin”—a virtual representation of a human patient that is dynamically updated with real-world data. This paper explores the potential of explainable AI (XAI) in conjunction with digital twins to create transparent, interpretable, and responsible healthcare solutions, particularly through privacy-preserving techniques like federated learning. By integrating advanced technologies such as computer vision, natural language processing, and machine learning, DHMs can be designed to understand, predict, and simulate the behaviours and requirements of individuals with varying abilities and backgrounds, ultimately creating personalised digital twins for enhanced healthcare.",

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author = "Mufti Mahmud and Brown, \{David J.\} and Yuan Shen and Rahman, \{Muhammad Arifur\} and Jun He and Kaiser, \{M. Shamim\} and Hamzah Luqman and Sajib Mistry and Noushath Shaffi and Vimbi Viswan and Rahman, \{M. Mostafizur\} and Mamun, \{Shamim Al\} and Tamanna Sharmeen and Rasha Alahmad and Aradhya, \{V. N.Manjunath\} and Hashmi, \{Mohammad Farukh\} and Shuqiang Wang and Cosimo Ieracitano and Nadia Mammone and Maryam Doborjeh and Kanad Ray",

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Mahmud, M, Brown, DJ, Shen, Y, Rahman, MA, He, J, Kaiser, MS, Luqman, H, Mistry, S, Shaffi, N, Viswan, V, Rahman, MM, Mamun, SA, Sharmeen, T, Alahmad, R, Aradhya, VNM, Hashmi, MF, Wang, S, Ieracitano, C, Mammone, N, Doborjeh, M & Ray, K 2026, Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence. in VG Duffy, Q Gao & J Zhou (eds), HCI International 2025 – Late Breaking Papers - 27th International Conference on Human-Computer Interaction, HCII 2025, Proceedings. Lecture Notes in Computer Science, vol. 16340 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 359-374, Late breaking papers from the 27th International Conference on Human-Computer Interaction, HCI International 2025, Gothenburg, Sweden, 22/06/25. https://doi.org/10.1007/978-3-032-13022-8_25

Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence. / Mahmud, Mufti; Brown, David J.; Shen, Yuan et al.
HCI International 2025 – Late Breaking Papers - 27th International Conference on Human-Computer Interaction, HCII 2025, Proceedings. ed. / Vincent G. Duffy; Qin Gao; Jia Zhou. Springer Science and Business Media Deutschland GmbH, 2026. p. 359-374 (Lecture Notes in Computer Science; Vol. 16340 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Mahmud M, Brown DJ, Shen Y, Rahman MA, He J, Kaiser MS et al. Preventive Healthcare Through Privacy-Preserving, Explainable and Inclusive Artificial Intelligence. In Duffy VG, Gao Q, Zhou J, editors, HCI International 2025 – Late Breaking Papers - 27th International Conference on Human-Computer Interaction, HCII 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2026. p. 359-374. (Lecture Notes in Computer Science). doi: 10.1007/978-3-032-13022-8_25