Foundation for Research and Technology Hellas

Bias in data‐driven artificial intelligence systems—An introductory survey

Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju et al.

2020 · 969 Zit.

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

Karim Lekadir, Alejandro F. Frangi, Antonio R. Porras et al.

2025 · 282 Zit.

The Integration of Artificial Intelligence into Clinical Practice

Vangelis Karalis

2024 · 215 Zit.

Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions

Tugba Akinci D’Antonoli, Arnaldo Stanzione, Christian Bluethgen et al.

2023 · 199 Zit.

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

Κωνσταντίνα Κούρου, Konstantinos Exarchos, Costas Papaloukas et al.

2021 · 149 Zit.

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Andreas Triantafyllidis, Haridimos Kondylakis, Dimitrios G. Katehakis et al.

2022 · 50 Zit.

Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology

Moreno Zanardo, Jacob J. Visser, Anna Colarieti et al.

2024 · 34 Zit.

Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper

Luis Martí‐Bonmatí, Dow‐Mu Koh, Katrine Riklund et al.

2022 · 32 Zit.

Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations

Burak Koçak, Michail E. Klontzas, Arnaldo Stanzione et al.

2025 · 26 Zit.

Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure

Luis Martí‐Bonmatí, Ignácio Blanquer, Manolis Tsiknakis et al.

2025 · 21 Zit.

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

Haridimos Kondylakis, Cristian Axenie, Dhundy Bastola et al.

2020 · 21 Zit.

Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

Woo Yeon Park, Kyulee Jeon, T Schmidt et al.

2024 · 20 Zit.

Recognising errors in AI implementation in radiology: A narrative review

Nikolaos Stogiannos, Renato Cuocolo, Tugba Akinci D’Antonoli et al.

2025 · 18 Zit.

Documenting the de-identification process of clinical and imaging data for AI for health imaging projects

Haridimos Kondylakis, R. Sanchís Catalán, Sara Martinez Alabart et al.

2024 · 18 Zit.

MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes

Varvara Kalokyri, Haridimos Kondylakis, Stelios Sfakianakis et al.

2023 · 15 Zit.