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20 results on '"Tsakou, Gianna"'

1. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Author

Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, and Starmans, Martijn P A

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.0, I.4.0, I.5.0
Abstract

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Published
2023

2. FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Author

Lekadir, Karim, Osuala, Richard, Gallin, Catherine, Lazrak, Noussair, Kushibar, Kaisar, Tsakou, Gianna, Aussó, Susanna, Alberich, Leonor Cerdá, Marias, Kostas, Tsiknakis, Manolis, Colantonio, Sara, Papanikolaou, Nickolas, Salahuddin, Zohaib, Woodruff, Henry C, Lambin, Philippe, and Martí-Bonmatí, Luis

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Addressing these concerns and risks, the FUTURE-AI framework has been proposed, which, sourced from a global multi-domain expert consensus, comprises guiding principles for increased trust, safety, and adoption for AI in healthcare. In this paper, we transform the general FUTURE-AI healthcare principles to a concise and specific AI implementation guide tailored to the needs of the medical imaging community. To this end, we carefully assess each building block of the FUTURE-AI framework consisting of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability, and respectively define concrete best practices based on accumulated AI implementation experiences from five large European projects on AI in Health Imaging. We accompany our concrete step-by-step medical imaging development guide with a practical AI solution maturity checklist, thus enabling AI development teams to design, evaluate, maintain, and deploy technically, clinically and ethically trustworthy imaging AI solutions into clinical practice., Comment: Please refer to arXiv:2309.12325 for the latest FUTURE-AI framework for healthcare

Published
2021

3. Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Author

Kondylakis, Haridimos, Kalokyri, Varvara, Sfakianakis, Stelios, Marias, Kostas, Tsiknakis, Manolis, Jimenez-Pastor, Ana, Camacho-Ramos, Eduardo, Blanquer, Ignacio, Segrelles, J. Damian, López-Huguet, Sergio, Barelle, Caroline, Kogut-Czarkowska, Magdalena, Tsakou, Gianna, Siopis, Nikolaos, Sakellariou, Zisis, Bizopoulos, Paschalis, Drossou, Vicky, Lalas, Antonios, Votis, Konstantinos, Mallol, Pedro, Marti-Bonmati, Luis, Alberich, Leonor Cerdá, Seymour, Karine, Boucher, Samuel, Ciarrocchi, Esther, Fromont, Lauren, Rambla, Jordi, Harms, Alexander, Gutierrez, Andrea, Starmans, Martijn P. A., Prior, Fred, Gelpi, Josep Ll., and Lekadir, Karim

Published
2023
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5. From Syntactic to Semantic Interoperability Using a Hyperontology in the Oncology Domain.

Author

EL GHOSH, Mirna, KALOKYRI, Varvara, SAMBRES, Mélanie, VATERKOWSKI, Morgan, DUCLOS, Catherine, TANNIER, Xavier, TSAKOU, Gianna, TSIKNAKIS, Manolis, DANIEL, Christel, and DHOMBRES, Ferdinand

Abstract

Interoperability is crucial to overcoming various challenges of data integration in the healthcare domain. While OMOP and FHIR data standards handle syntactic heterogeneity among heterogeneous data sources, ontologies support semantic interoperability to overcome the complexity and disparity of healthcare data. This study proposes an ontological approach in the context of the EUCAIM project to support semantic interoperability among distributed big data repositories that have applied heterogeneous cancer image data models using a semantically well-founded Hyperontology for the oncology domain. [ABSTRACT FROM AUTHOR]

Published
2024
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6. INSPIRE: Evaluation of a Smart-Home System for Infotainment Management and Device Control

Author

Moeller, Sebastian, Krebber, Jan, Raake, Alexander, Smeele, Paula, Rajman, Martin, Melichar, Mirek, Pallotta, Vincenzo, Tsakou, Gianna, Kladis, Basilis, Vovos, Anestis, Hoonhout, Jettie, Schuchardt, Dietmar, Fakotakis, Nikos, Ganchev, Todor, and Potamitis, Ilyas

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computation and Language, H.5.2, I.2.7, H.1.2
Abstract

This paper gives an overview of the assessment and evaluation methods which have been used to determine the quality of the INSPIRE smart home system. The system allows different home appliances to be controlled via speech, and consists of speech and speaker recognition, speech understanding, dialogue management, and speech output components. The performance of these components is first assessed individually, and then the entire system is evaluated in an interaction experiment with test users. Initial results of the assessment and evaluation are given, in particular with respect to the transmission channel impact on speech and speaker recognition, and the assessment of speech output for different system metaphors., Comment: 4 pages

Published
2004

8. Data infrastructures for AI in medical imaging:a report on the experiences of five EU projects

Author

Kondylakis, Haridimos, Kalokyri, Varvara, Sfakianakis, Stelios, Marias, Kostas, Tsiknakis, Manolis, Jimenez-Pastor, Ana, Camacho-Ramos, Eduardo, Blanquer, Ignacio, Segrelles, J. Damian, López-Huguet, Sergio, Barelle, Caroline, Kogut-Czarkowska, Magdalena, Tsakou, Gianna, Siopis, Nikolaos, Sakellariou, Zisis, Bizopoulos, Paschalis, Drossou, Vicky, Lalas, Antonios, Votis, Konstantinos, Mallol, Pedro, Marti-Bonmati, Luis, Alberich, Leonor Cerdá, Seymour, Karine, Boucher, Samuel, Ciarrocchi, Esther, Fromont, Lauren, Rambla, Jordi, Harms, Alexander, Gutierrez, Andrea, Starmans, Martijn P.A., Prior, Fred, Gelpi, Josep Ll, Lekadir, Karim, Kondylakis, Haridimos, Kalokyri, Varvara, Sfakianakis, Stelios, Marias, Kostas, Tsiknakis, Manolis, Jimenez-Pastor, Ana, Camacho-Ramos, Eduardo, Blanquer, Ignacio, Segrelles, J. Damian, López-Huguet, Sergio, Barelle, Caroline, Kogut-Czarkowska, Magdalena, Tsakou, Gianna, Siopis, Nikolaos, Sakellariou, Zisis, Bizopoulos, Paschalis, Drossou, Vicky, Lalas, Antonios, Votis, Konstantinos, Mallol, Pedro, Marti-Bonmati, Luis, Alberich, Leonor Cerdá, Seymour, Karine, Boucher, Samuel, Ciarrocchi, Esther, Fromont, Lauren, Rambla, Jordi, Harms, Alexander, Gutierrez, Andrea, Starmans, Martijn P.A., Prior, Fred, Gelpi, Josep Ll, and Lekadir, Karim

Abstract

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area. Key points • Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata. • Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data. • Developing a common data model for storing all relevant information is a challenge. • Trust of data providers in data sharing initiatives is essential. • An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Published
2023

9. Prosperity as a Model for Next-Generation Accessibility

Author

Vanderheiden, Gregg C., Treviranus, Jutta, Clark, Colin, Peissner, Matthias, Tsakou, Gianna, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Stephanidis, Constantine, editor, and Antona, Margherita, editor

Published
2014
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10. A Novel Infrastructure Facilitating Access to, Charging, Ordering and Funding of Assistive Services

Author

Tsakou, Gianna, Leligou, Helen C., Katevas, Nikos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Stephanidis, Constantine, editor, and Antona, Margherita, editor

Published
2014
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11. Prosperity4All – Setting the Stage for a Paradigm Shift in eInclusion

Author

Peissner, Matthias, Vanderheiden, Gregg C., Treviranus, Jutta, Tsakou, Gianna, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Stephanidis, Constantine, editor, and Antona, Margherita, editor

Published
2014
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12. HOMEdotOLD, HOME Services aDvancing the sOcial inTeractiOn of eLDerly People

Author

Perakis, Konstantinos, Tsakou, Gianna, Kavvadias, Christoforos, Giannakoulias, Alkis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bravo, José, editor, Hervás, Ramón, editor, and Villarreal, Vladimir, editor

Published
2011
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18. Open developer space: An enabling infrastructure for stakeholders to generate new access solutions

Author

Vanderheiden, Gregg, Riedel, Till, Peissner, Matthias, Clark, Colin B.D., Peinado, Ignacio, Atkins, Tony, Tsakou, Gianna, Basman, Antranig, Bates, Simon, Gill, Avtar, Vanderheiden, Gregg, Riedel, Till, Peissner, Matthias, Clark, Colin B.D., Peinado, Ignacio, Atkins, Tony, Tsakou, Gianna, Basman, Antranig, Bates, Simon, and Gill, Avtar

Abstract

The DeveloperSpace, one of the core components of GPII, is a self-sustainable infrastructure and collaborative environment, where developers, implementers, consumers, prosumers and other directly and indirectly involved actors (e.g. teachers, caregivers, clinicians) may interact with and play a role in its viability and the development of new access solutions.

Published
2017

19. Using the Assistance on Demand Platform to Set Up a Network of Assistance Services.

Author

LELIGOU, Helen C., PANAGIOTIS, Athanasoulis, TSAKOU, Gianna, VANDERHEIDEN, Gregg, KOCSIS, Otilia, and KATEVAS, Nikos

Abstract

The Assistance on Demand (AoD) platform is a novel open-source infrastructure which enables the set-up and web publication of assistance services. This paper focuses on the potential of the AoD functionality to enable the configuration and creation of a Network of Assistance Services (NAS) by nonexpert users (e.g. consumers, family members). [ABSTRACT FROM AUTHOR]

Published
2017
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20. Open DeveloperSpace: An Enabling Infrastructure for Stakeholders to Generate New Access Solutions.

Author

VANDERHEIDEN, Gregg, RIEDEL, Till, PEISSNER, Matthias, CLARK, Colin, PEINADO, Ignacio, ATKINS, Tony, TSAKOU, Gianna, BASMAN, Antranig, BATES, Simon, and GILL, Avtar

Abstract

The DeveloperSpace, one of the core components of GPII, is a selfsustainable infrastructure and collaborative environment, where developers, implementers, consumers, prosumers and other directly and indirectly involved actors (e.g. teachers, caregivers, clinicians) may interact with and play a role in its viability and the development of new access solutions. [ABSTRACT FROM AUTHOR]

Published
2017
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