Freie Universität Berlin
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Swarm Learning for decentralized and confidential clinical machine learning
Stefanie Warnat‐Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry et al.
2021 · 822 Zit.
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities
Christian Meske, Enrico Bunde, Johannes Schneider et al.
2020 · 371 Zit.
Bias in AI-based models for medical applications: challenges and mitigation strategies
Mirja Mittermaier, Marium Raza, Joseph C. Kvedar
2023 · 330 Zit.
Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review
Kathrin Seibert, Dominik Domhoff, Dominik Bruch et al.
2021 · 276 Zit.
Leveraging Large Language Models for Decision Support in Personalized Oncology
Manuela Benary, Xing David Wang, Max Schmidt et al.
2023 · 273 Zit.
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Burak Koçak, Tugba Akinci D’Antonoli, Nathaniel D. Mercaldo et al.
2024 · 258 Zit.
Current applications and challenges in large language models for patient care: a systematic review
Felix Busch, Lena Hoffmann, Christopher Rueger et al.
2025 · 189 Zit.
The explainability paradox: Challenges for xAI in digital pathology
Theodore Evans, Carl Orge Retzlaff, Christian Geißler et al.
2022 · 134 Zit.
Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy
Jesús Gómez Rossi, Natalia Rojas-Perilla, Joachim Krois et al.
2022 · 121 Zit.
Toward Explainable Artificial Intelligence for Precision Pathology
Frederick Klauschen, Jonas Dippel, Philipp Keyl et al.
2023 · 115 Zit.
Triage Accuracy of Symptom Checker Apps: 5-Year Follow-up Evaluation
Malte L Schmieding, Marvin Kopka, Konrad Schmidt et al.
2022 · 110 Zit.
Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study
Akira-Sebastian Poncette, Daniel Leon Glauert, Lina Mosch et al.
2020 · 105 Zit.
Global healthcare fairness: We should be sharing more, not less, data
Kenneth P. Seastedt, Patrick Schwab, Zach O’Brien et al.
2022 · 87 Zit.
Explainability and causability in digital pathology
Markus Plass, Michaela Kargl, Tim‐Rasmus Kiehl et al.
2023 · 85 Zit.
Machine Learning in Dentistry: A Scoping Review
Lubaina T. Arsiwala-Scheppach, Akhilanand Chaurasia, Anne Müller et al.
2023 · 81 Zit.