Medical University of Vienna

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Gary S. Collins, Karel G.M. Moons, Paula Dhiman et al.

2024 · 1.422 Zit.

Continuous Learning AI in Radiology: Implementation Principles and Early Applications

Oleg S. Pianykh, Georg Langs, Marc Dewey et al.

2020 · 221 Zit.

Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis

Christian Leibig, Moritz Brehmer, Stefan Bunk et al.

2022 · 183 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 · 175 Zit.

Artificial Intelligence and Public Health: An Exploratory Study

David Jungwirth, Daniela Haluza

2023 · 136 Zit.

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

Maarten van Smeden, Georg Heinze, Ben Van Calster et al.

2022 · 129 Zit.

ChatGPT: promise and challenges for deployment in low- and middle-income countries

Xiaofei Wang, Hayley M. Sanders, Yuchen Liu et al.

2023 · 124 Zit.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Tirtha Chanda, Katja Hauser, Sarah Hobelsberger et al.

2024 · 123 Zit.

Safety and Efficacy of Darbepoetin Alfa in Previously Untreated Extensive-Stage Small-Cell Lung Cancer Treated With Platinum Plus Etoposide

Robert Pirker, Rodryg Ramlau, Wolfgang Schuette et al.

2008 · 115 Zit.

Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI)

Janne Cadamuro, Federico Cabitza, Željko Debeljak et al.

2023 · 109 Zit.

Attitudes towards artificial intelligence within dermatology: an international online survey

Sam Polesie, Martin Gillstedt, Harald Kittler et al.

2020 · 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 · 81 Zit.

Artificial intelligence in cardiology

Diana Bonderman

2017 · 72 Zit.

Machine learning: from radiomics to discovery and routine

Georg Langs, Sebastian Röhrich, Johannes Hofmanninger et al.

2018 · 68 Zit.

Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs

Santiago Vitale, José Ignacio Orlando, Emmanuel Iarussi et al.

2019 · 62 Zit.