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Collaborative Forensic Autopsy Documentation and Supervised Report Generation Using a Hybrid Mixed-Reality Environment and Generative AI
Monash Univ, Australia.
Univ Queensland, Australia.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7207-1276
Monash Univ, Australia; Victorian Inst Forens Med, Australia.
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2024 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 30, no 11, p. 7452-7462Article in journal (Refereed) Published
Abstract [en]

Forensic investigation is a complex procedure involving experts working together to establish cause of death and report findings to legal authorities. While new technologies are being developed to provide better post-mortem imaging capabilities-including mixed-reality (MR) tools to support 3D visualisation of such data-these tools do not integrate seamlessly into their existing collaborative workflow and report authoring process, requiring extra steps, e.g. to extract imagery from the MR tool and combine with physical autopsy findings for inclusion in the report. Therefore, in this work we design and evaluate a new forensic autopsy report generation workflow and present a novel documentation system using hybrid mixed-reality approaches to integrate visualisation, voice and hand interaction, as well as collaboration and procedure recording. Our preliminary findings indicate that this approach has the potential to improve data management, aid reviewability, and thus, achieve more robust standards. Further, it potentially streamlines report generation and minimise dependency on external tools and assistance, reducing autopsy time and related costs. This system also offers significant potential for education.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2024. Vol. 30, no 11, p. 7452-7462
Keywords [en]
Forensic autopsy; report generation; documentation; documentation; mixed reality; mixed reality; generative AI; generative AI; mixed reality; generative AI
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-209067DOI: 10.1109/TVCG.2024.3456212ISI: 001338569300043PubMedID: 39250385OAI: oai:DiVA.org:liu-209067DiVA, id: diva2:1910514
Note

Funding Agencies|VIFM; Knut and Alice Wallenberg Foundation [KAW 2019.0024]

Available from: 2024-11-05 Created: 2024-11-05 Last updated: 2024-11-05

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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Output format
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