Berlin Institute of Health at Charité - Universitätsmedizin Berlin

The Hong Kong Principles for assessing researchers: Fostering research integrity

David Moher, L.M. Bouter, Sabine Kleinert et al.

2020 · 509 Zit.

Why digital medicine depends on interoperability

Moritz Lehne, Julian Saß, Andrea Essenwanger et al.

2019 · 461 Zit.

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Baptiste Vasey, Myura Nagendran, Bruce Campbell et al.

2022 · 436 Zit.

Artificial intelligence in dental research: Checklist for authors, reviewers, readers

Falk Schwendicke, Tarry Singh, Jae‐Hong Lee et al.

2021 · 327 Zit.

Bias in AI-based models for medical applications: challenges and mitigation strategies

Mirja Mittermaier, Marium Raza, Joseph C. Kvedar

2023 · 310 Zit.

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

Julia Amann, Dennis Vetter, Stig Nikolaj Fasmer Blomberg et al.

2022 · 191 Zit.

Benchmark evaluation of DeepSeek large language models in clinical decision-making

Sarah Sandmann, Stefan Hegselmann, Michael Fujarski et al.

2025 · 116 Zit.

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

Marieke Bak, Vince I. Madai, Marie-Christine Fritzsche et al.

2022 · 107 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 · 104 Zit.

Ethical considerations on artificial intelligence in dentistry: A framework and checklist

Rata Rokhshad, Maxime Ducret, Akhilanand Chaurasia et al.

2023 · 101 Zit.

From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare

David Higgins, Vince I. Madai

2020 · 101 Zit.

Artificial intelligence for oral and dental healthcare: Core education curriculum

Falk Schwendicke, Akhilanand Chaurasia, Thomas Wiegand et al.

2022 · 93 Zit.

A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare

Jana Fehr, Brian Citro, Rohit Malpani et al.

2024 · 88 Zit.

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

Julia Amann, Effy Vayena, Kelly E. Ormond et al.

2023 · 87 Zit.

Global healthcare fairness: We should be sharing more, not less, data

Kenneth P. Seastedt, Patrick Schwab, Zach O’Brien et al.

2022 · 84 Zit.