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

Autor: 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, Starmans, Martijn P A

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, clinica

Externí odkaz: http://arxiv.org/abs/2309.12325

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Autor: Papagiannopoulos, Orestis D., Pezoulas, Vasileios C., Papaloukas, Costas, Fotiadis, Dimitrios I.

Publikováno v: In Computational and Structural Biotechnology Journal December 2024 23:2152-2162

Identification and evolution of predictors of Sjögren's disease-associated mucosa-associated lymphoid tissue lymphoma development over time: a case-control study

Autor: Goules, Andreas V ^*, Chatzis, Loukas, Pezoulas, Vasilis C, Patsouras, Markos, Mavragani, Clio, Quartuccio, Luca, Baldini, Chiara, De Vita, Salvatore, Fotiadis, Dimitrios I, Tzioufas, Athanasios G

Publikováno v: In The Lancet Rheumatology October 2024 6(10):e693-e702

ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions

Autor: Zaridis, Dimitrios I., Mylona, Eugenia, Tsiknakis, Nikos, Tachos, Nikolaos S., Matsopoulos, George K., Marias, Kostas, Tsiknakis, Manolis, Fotiadis, Dimitrios I.

Publikováno v: In Patterns 12 July 2024 5(7)

A new smart-cropping pipeline for prostate segmentation using deep learning networks

Autor: Zaridis, Dimitrios G., Mylona, Eugenia, Tachos, Nikolaos S., Marias, Kostas, Papanikolaou, Nikolaos, Tsiknakis, Manolis, Fotiadis, Dimitrios I.

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of t

Externí odkaz: http://arxiv.org/abs/2107.02476