Artificial intelligence for optimizing cancer imaging: user experience study

Iman Hesso; Lithin Zacharias; Reem Kayyali; Andreas Charalambous; Maria Lavdaniti; Evangelia Stalika; Tarek Ajami; Wanda Acampa; Jasmina Boban; Shereen Nabhani-Gebara

doi:10.2196/52639

Artificial intelligence for optimizing cancer imaging: user experience study

Iman Hesso
, Lithin Zacharias
, Reem Kayyali
, Andreas Charalambous
, Maria Lavdaniti
, Evangelia Stalika
, Tarek Ajami
, Wanda Acampa
, Jasmina Boban
, Shereen Nabhani-Gebara

Kingston University
Cyprus University of Technology
International Hellenic University
Hospital Clinic de Barcelona
University of Naples Federico II
University of Novi Sad

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	e52639
Journal	JMIR Cancer
Volume	10
DOIs	https://doi.org/10.2196/52639
Publication status	Published - 10 Oct 2024

Keywords

Cancer studies

Access to Document

10.2196/52639

https://doi.org/10.2196/52639

INCISIVE: A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer
El Nabhani, S. (PI)
1/10/20 → 31/03/24
Project: Research

Cite this

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title = "Artificial intelligence for optimizing cancer imaging: user experience study",

keywords = "Cancer studies",

author = "Iman Hesso and Lithin Zacharias and Reem Kayyali and Andreas Charalambous and Maria Lavdaniti and Evangelia Stalika and Tarek Ajami and Wanda Acampa and Jasmina Boban and Shereen Nabhani-Gebara",

year = "2024",

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doi = "10.2196/52639",

language = "English",

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T2 - user experience study

AU - Hesso, Iman

AU - Zacharias, Lithin

AU - Kayyali, Reem

AU - Charalambous, Andreas

AU - Lavdaniti, Maria

AU - Stalika, Evangelia

AU - Ajami, Tarek

AU - Acampa, Wanda

AU - Boban, Jasmina

AU - Nabhani-Gebara, Shereen

PY - 2024/10/10

Y1 - 2024/10/10

KW - Cancer studies

U2 - 10.2196/52639

DO - 10.2196/52639

M3 - Article

SN - 2369-1999

VL - 10

JO - JMIR Cancer

JF - JMIR Cancer

M1 - e52639

ER -

Artificial intelligence for optimizing cancer imaging: user experience study

Keywords

Access to Document

Projects

INCISIVE: A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer

Cite this