Oxford University Hospitals NHS Trust

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Evangelia Christodoulou, Jie Ma, Gary S. Collins et al.

2019 · 1.879 Zit.

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

Myura Nagendran, Yang Chen, Christopher A. Lovejoy et al.

2020 · 1.022 Zit.

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

Madison Milne‐Ives, Caroline de Cock, Ernest Lim et al.

2020 · 543 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 · 456 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 · 338 Zit.

The value of standards for health datasets in artificial intelligence-based applications

Anmol Arora, Joseph Alderman, Joanne Palmer et al.

2023 · 250 Zit.

Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions

Michael Phillips, Helen Marsden, W. Jaffe et al.

2019 · 217 Zit.

Predictive analytics in health care: how can we know it works?

Ben Van Calster, Laure Wynants, D. Timmerman et al.

2019 · 210 Zit.

ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist

Amir Jamaludin, Meelis Lootus, Timor Kadir et al.

2017 · 207 Zit.

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

Fatemeh Rahimian, Gholamreza Salimi‐Khorshidi, Amir H. Payberah et al.

2018 · 146 Zit.

Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems

Baptiste Vasey, Stephan Ursprung, Benjamin Beddoe et al.

2021 · 137 Zit.

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Joseph Alderman, Joanne Palmer, Elinor Laws et al.

2024 · 101 Zit.

Calculating the probability of random sampling for continuous variables in submitted or published randomised controlled trials

J. B. Carlisle, Franklin Dexter, Jaideep J. Pandit et al.

2015 · 84 Zit.

Tackling bias in AI health datasets through the STANDING Together initiative

Shaswath Ganapathi, Joanne Palmer, Joseph Alderman et al.

2022 · 80 Zit.

Generalizability assessment of AI models across hospitals in a low-middle and high income country

Jenny Yang, Nguyen Thanh Dung, Phạm Ngọc Thạch et al.

2024 · 71 Zit.