Sorbonne Université
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser et al.
2019 · 8.380 Zit.
Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France
M. Lai, M Brian, Marie‐France Mamzer
2020 · 290 Zit.
Open science saves lives: lessons from the COVID-19 pandemic
Lonni Besançon, Nathan Peiffer‐Smadja, Corentin Ségalas et al.
2021 · 259 Zit.
Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence
Ali Guermazi, Chadi Tannoury, Andrew J. Kompel et al.
2021 · 247 Zit.
COVID-19-related medical research: a meta-research and critical appraisal
Marc Raynaud, Huanxi Zhang, Kévin Louis et al.
2021 · 181 Zit.
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella et al.
2020 · 158 Zit.
Machine learning and artificial intelligence in haematology
Roni Shouval, Joshua Fein, Bipin N. Savani et al.
2020 · 118 Zit.
Comparative benchmarking of the DeepSeek large language model on medical tasks and clinical reasoning
Mickaël Tordjman, Zelong Liu, Murat Yüce et al.
2025 · 105 Zit.
Prompt Engineering Paradigms for Medical Applications: Scoping Review
Jamil Zaghir, Marco Naguib, Mina Bjelogrlic et al.
2024 · 87 Zit.
EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
Laure Gossec, Joanna Kedra, H. Servy et al.
2019 · 82 Zit.
Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality?
Tamim Alsuliman, Dania Humaidan, Layth Sliman
2020 · 82 Zit.
Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
Ferdinand Dhombres, Jules Bonnard, Kévin Bailly et al.
2022 · 76 Zit.
Machine Learning for COVID-19 needs global collaboration and data-sharing
Nathan Peiffer‐Smadja, Redwan Maatoug, François-Xavier Lescure et al.
2020 · 76 Zit.
The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence
Federico Cabitza, Jean‐David Zeitoun
2019 · 69 Zit.
Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence–Based Health Technologies: What Healthcare Stakeholders Need to Know
Line Farah, Juliette Murris, Isabelle Borget et al.
2023 · 63 Zit.