Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.

By John T Mongan, Linda Moy, Charles E. Kahn Jr.. Radiology: Artificial Intelligence, 2020

1,137 citations recorded by the source corpus.

2 Structured summary of study design, methods, results, and conclusions

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5b34da942633f3a3afe01386341b8839a985ca0d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gary S. Collins
2. Ben Glocker
3. Geert J. S. Litjens
4. Anne L. Martel
5. Michael Alexander Riegler
6. Shangqing Liu
7. Klaus Hermann Maier-Hein
8. Lena Maier-Hein

In Health Informatics

1. Gary S. Collins
2. Karel G. M. Moons

In Radiology, Nuclear Medicine and Imaging

1. Gary S. Collins
2. B. van Ginneken
3. Fabian Isensee
4. Klaus Hermann Maier-Hein
5. Geert J. S. Litjens
6. Michael Alexander Riegler
7. Alan L. Yuille
8. Tal Arbel

Citations in the corpus

• Metrics Reloaded: Recommendations for image analysis validation (2022, positive): “Make uncertainties in rankings (if any) explicit [93] Depending on the specific application, apply existing reporting guidelines, such as BIAS [59] (for challenges), TRIPOD [67] (for validation of prediction models, including recommendations that are applicable to the validation of medical image ana”
• Leakage and the reproducibility crisis in machine-learning-based science (2023, background): “Further, most checklists currently in use are not developed for ML-based science in general but rather for specific scientific or research communities.(4,38) As a result, best practices for model reporting in ML-based science are underspecified.”
• CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “, performance on new data across many hospitals, rather than the performance tailored to a single dataset [39, 42].”
• AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? (2020, background): “reach high accuracy, but also can generalize well on data from different sources [8], [9].”
• FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2023, neutral): “…data, evaluation criteria and results, biases and other limitations, and periodic audits and updates 49-51 ; (c) a publication based on existing AI reporting the bmj | BMJ 2025;388:e081554 | doi: 10.1136/bmj-2024-081554 standards 13 15 52 ; and (d) a risk management file (see traceability 1).”
• Bias in artificial intelligence algorithms and recommendations for mitigation (2023, methodology): “A number of comprehensive frameworks and checklists have been developed, such as the Translational Evaluation of Healthcare AI (TEHAI) [46], the DECIDE-AI [47], the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) [48], the prediction model risk of bias assessment tool”