Karolinska Institutet

Deep learning based tissue analysis predicts outcome in colorectal cancer

Dmitrii Bychkov, Nina Linder, Riku Turkki et al.

2018 · 642 Zit.

Virtual Patient Simulations in Health Professions Education: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration

Andrzej A. Kononowicz, Luke Woodham, Samuel Edelbring et al.

2019 · 517 Zit.

Artificial intelligence for analyzing orthopedic trauma radiographs

Jakub Olczak, Niklas Fahlberg, Atsuto Maki et al.

2017 · 446 Zit.

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen et al.

2022 · 446 Zit.

Artificial intelligence as the next step towards precision pathology

Balázs Ács, Mattias Rantalainen, Johan Hartman

2020 · 431 Zit.

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen et al.

2022 · 390 Zit.

Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

Karin Dembrower, Erik Wåhlin, Yue Liu et al.

2020 · 277 Zit.

Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study

Karin Dembrower, Alessio Crippa, Eugenia Colón et al.

2023 · 277 Zit.

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Malvika Sharma, Carl Savage, Monika Nair et al.

2022 · 180 Zit.

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review

Rasheed Omobolaji Alabi, Omar Youssef, Matti Pirinen et al.

2021 · 168 Zit.

The diagnostic and triage accuracy of digital and online symptom checker tools: a systematic review

William Wallace, Calvin Chan, Swathikan Chidambaram et al.

2022 · 154 Zit.

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Wouter Bulten, Maschenka Balkenhol, Jean-Joël Awoumou Belinga et al.

2020 · 147 Zit.

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal

Jakub Olczak, John Pavlopoulos, Jasper Prijs et al.

2021 · 126 Zit.

Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study

T Wilhelm, Jonas Roos, Robert Kaczmarczyk

2023 · 123 Zit.

Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models

Nesaretnam Barr Kumarakulasinghe, Tobias Blomberg, Jintai Liu et al.

2020 · 121 Zit.