Heidelberg University
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li et al.
2020 · 2.326 Zit.
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas et al.
2022 · 1.143 Zit.
The future landscape of large language models in medicine
Jan Clusmann, Fiona R. Kolbinger, Hannah Sophie Muti et al.
2023 · 892 Zit.
Surgical data science for next-generation interventions
Lena Maier‐Hein, S. Swaroop Vedula, Stefanie Speidel et al.
2017 · 512 Zit.
Regulating ChatGPT and other Large Generative AI Models
Philipp Hacker, Andreas Engel, Marco Mauer
2023 · 395 Zit.
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
Burak Koçak, Bettina Baeßler, Spyridon Bakas et al.
2023 · 394 Zit.
Metrics reloaded: recommendations for image analysis validation
Lena Maier‐Hein, Annika Reinke, Patrick Godau et al.
2024 · 359 Zit.
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier‐Hein, Matthias Eisenmann, Annika Reinke et al.
2018 · 352 Zit.
Surgical data science – from concepts toward clinical translation
Lena Maier‐Hein, Matthias Eisenmann, Duygu Sarıkaya et al.
2022 · 315 Zit.
Benefits and challenges of Big Data in healthcare: an overview of the European initiatives
Roberta Pastorino, Corrado De Vito, Giuseppe Migliara et al.
2019 · 302 Zit.
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
Qi Dou, Tiffany Y. So, Meirui Jiang et al.
2021 · 269 Zit.
Machine learning in clinical decision making
Lorenz Adlung, Yotam Cohen, Uria Mor et al.
2021 · 265 Zit.
Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review
Tadeusz Ciecierski-Holmes, Ritvij Singh, Miriam Axt et al.
2022 · 248 Zit.
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul et al.
2025 · 247 Zit.
Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review
Steven W J Nijman, Artuur Leeuwenberg, Inés Beekers et al.
2021 · 234 Zit.