Lotty Hooft

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

2021 · 739 Zit. · BMJ Open

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

2022 · 451 Zit. · npj Digital Medicine

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

2021 · 332 Zit. · BMJ

Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

2021 · 284 Zit. · BMJ Open

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

2025 · 186 Zit. · BMJ

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

2022 · 125 Zit. · BMC Medical Research Methodology

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

2022 · 119 Zit. · BMC Medical Research Methodology

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

2021 · 106 Zit. · Journal of Clinical Epidemiology

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

2022 · 89 Zit. · Journal of Clinical Epidemiology

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques

2020 · 84 Zit. · BMJ Open

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

2022 · 61 Zit. · Diagnostic and Prognostic Research

Systematic review finds “spin” practices and poor reporting standards in studies on machine learning-based prediction models

2023 · 56 Zit. · Journal of Clinical Epidemiology

STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration

2022 · 52 Zit. · Вопросы современной педиатрии

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

2025 · 47 Zit. · Nature Medicine

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

2023 · 36 Zit. · Journal of Clinical Epidemiology