4.4 Review

DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology

Related references

Note: Only part of the references are listed.
Article Biochemistry & Molecular Biology

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

Baptiste Vasey et al.

Summary: The article introduces the DECIDE-AI checklist, which includes key items that should be reported in early-stage clinical studies of AI-based decision support systems. It emphasizes the importance of responsible and transparent deployment of AI systems in healthcare. The checklist was developed through a consensus-based process involving multiple stakeholders.

NATURE MEDICINE (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

Ali Guermazi et al.

Summary: This study aimed to evaluate the impact of assistance by artificial intelligence (AI) on the diagnosis of fractures on radiographs. The results showed that AI assistance improved the sensitivity of physicians in detecting fractures by 10.4% without affecting specificity. Additionally, AI reduced the average reading time by 6.3 seconds.

RADIOLOGY (2022)

Letter Biochemistry & Molecular Biology

DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence

Baptiste Vasey et al.

Summary: With the advancement of clinical decision-support systems powered by artificial intelligence, there is a need for improved guidance on reporting human factors and early-stage clinical evaluation.

NATURE MEDICINE (2021)

Article Clinical Neurology

Noncontrast Computed Tomography e-Stroke Infarct Volume Is Similar to RAPID Computed Tomography Perfusion in Estimating Postreperfusion Infarct Volumes

Mehdi Bouslama et al.

Summary: The study compared the performance of NCCT e-Stroke software and RAPID CTP in predicting postreperfusion infarct volumes and clinical outcomes, showing similar results for both tools and across different time windows, suggesting that NCCT e-Stroke software could be a viable alternative in centers with limited access to advanced imaging. Further development of fusion maps may enhance the performance of these tools when used together.

STROKE (2021)

Review Medicine, General & Internal

Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems A Systematic Review

Baptiste Vasey et al.

Summary: This systematic review found limited evidence of the association between the use of ML-based CDSSs and improved clinician diagnostic performance. Most studies had a small number of participants, were at high or unclear risk of bias, and lacked consideration for human factors. Caution should be taken when assessing the potential of ML to enhance human diagnostic performance, and more comprehensive evaluation is needed before implementing ML-based CDSSs in clinical settings.

JAMA NETWORK OPEN (2021)

Article Medicine, General & Internal

A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance

Kathryn Skivington et al.

Summary: The UK Medical Research Council's new guidance framework for developing and evaluating complex interventions, commissioned jointly with the National Institute for Health Research, considers recent developments in theory and methods to maximize research efficiency, use, and impact.

BMJ-BRITISH MEDICAL JOURNAL (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

Kicky G. van Leeuwen et al.

Summary: Among the 100 CE-marked AI products in the field of radiology, 64 lack peer-reviewed evidence, with only 18 proving to have potential clinical impact.

EUROPEAN RADIOLOGY (2021)

Article Biochemistry & Molecular Biology

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer

Chris McIntosh et al.

Summary: The study demonstrates the potential impact of machine learning in healthcare delivery, with a random forest algorithm applied to therapeutic radiation therapy planning for prostate cancer. Machine-generated RT plans were found to be clinically acceptable in 89% of cases and were selected over human-generated plans in 72% of head-to-head comparisons, significantly reducing the time required for RT planning.

NATURE MEDICINE (2021)

Review Health Care Sciences & Services

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

Ravi Aggarwal et al.

Summary: This study evaluated the diagnostic accuracy of deep learning algorithms in identifying pathology in medical imaging, finding good performance in ophthalmology, respiratory disease and breast imaging, but noting high heterogeneity between studies and extensive variation in methodology, outcome measures and terminology. The development of artificial intelligence-specific guidelines, particularly STARD, is recommended to address these issues.

NPJ DIGITAL MEDICINE (2021)

Editorial Material Biochemistry & Molecular Biology

Clinical research underlies ethical integration of healthcare artificial intelligence

Melissa D. McCradden et al.

NATURE MEDICINE (2020)

Article Biochemistry & Molecular Biology

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Samantha Cruz Rivera et al.

NATURE MEDICINE (2020)

Article Health Care Sciences & Services

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Stan Benjamens et al.

NPJ DIGITAL MEDICINE (2020)

Article Health Care Sciences & Services

0 Evaluating artificial intelligence in medicine: phases of clinical research

Yoonyoung Park et al.

JAMIA OPEN (2020)

Article Automation & Control Systems

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

David Higgins et al.

ADVANCED INTELLIGENT SYSTEMS (2020)

Editorial Material Medicine, General & Internal

Reporting of artificial intelligence prediction models

Gary S. Collins et al.

LANCET (2019)

Article Medicine, General & Internal

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Robert F. Wolff et al.

ANNALS OF INTERNAL MEDICINE (2019)

Article Cell Biology

Detecting human coronary inflammation by imaging perivascular fat

Alexios S. Antonopoulos et al.

SCIENCE TRANSLATIONAL MEDICINE (2017)

Editorial Material Medicine, General & Internal

IDEAL-D: a rational framework for evaluating and regulating the use of medical devices

Art Sedrakyan et al.

BMJ-BRITISH MEDICAL JOURNAL (2016)

Article Medicine, General & Internal

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

Jonathan A. C. Sterne et al.

BMJ-BRITISH MEDICAL JOURNAL (2016)

Editorial Material Medicine, General & Internal

IDEAL-D: a rational framework for evaluating and regulating the use of medical devices

Art Sedrakyan et al.

BMJ-BRITISH MEDICAL JOURNAL (2016)

Article Medicine, General & Internal

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

Jonathan A. C. Sterne et al.

BMJ-BRITISH MEDICAL JOURNAL (2016)

Article Medicine, General & Internal

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

Patrick M. Bossuyt et al.

BMJ-BRITISH MEDICAL JOURNAL (2015)

Article Medicine, General & Internal

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

Patrick M. Bossuyt et al.

BMJ-BRITISH MEDICAL JOURNAL (2015)

Editorial Material Medicine, Research & Experimental

Diagnostic randomized controlled trials: the final frontier

Marc Rodger et al.

TRIALS (2012)

Article Medicine, General & Internal

QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies

Penny F. Whiting et al.

ANNALS OF INTERNAL MEDICINE (2011)

Article Medicine, General & Internal

CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials

Kenneth F. Schulz et al.

BMJ-BRITISH MEDICAL JOURNAL (2010)

Article Medicine, General & Internal

Developing and evaluating complex interventions: the new Medical Research Council guidance

Peter Craig et al.

BMJ-BRITISH MEDICAL JOURNAL (2008)

Article Medicine, General & Internal

Framework for design and evaluation of complex interventions to improve health

M Campbell et al.

BRITISH MEDICAL JOURNAL (2000)