The current and future state of AI interpretation of medical images
P Rajpurkar, MP Lungren - New England Journal of Medicine, 2023 - Mass Medical Soc
The Current and Future State of AI Interpretation of Medical Images | New England Journal of
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review
AAH de Hond, AM Leeuwenberg, L Hooft… - NPJ digital …, 2022 - nature.com
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …
driven prediction models requires careful quality and applicability assessment before they …
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
Even though radiomics can hold great potential for supporting clinical decision-making, its
current use is mostly limited to academic research, without applications in routine clinical …
current use is mostly limited to academic research, without applications in routine clinical …
Clip-driven universal model for organ segmentation and tumor detection
An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …
segmentation and tumor detection. However, due to the small size and partially labeled …
[HTML][HTML] Leakage and the reproducibility crisis in machine-learning-based science
S Kapoor, A Narayanan - Patterns, 2023 - cell.com
Machine-learning (ML) methods have gained prominence in the quantitative sciences.
However, there are many known methodological pitfalls, including data leakage, in ML …
However, there are many known methodological pitfalls, including data leakage, in ML …
External validation of deep learning algorithms for radiologic diagnosis: a systematic review
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
Radiomics in oncology: a practical guide
Radiomics refers to the extraction of mineable data from medical imaging and has been
applied within oncology to improve diagnosis, prognostication, and clinical decision support …
applied within oncology to improve diagnosis, prognostication, and clinical decision support …
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Abstract Machine learning methods offer great promise for fast and accurate detection and
prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest …
prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest …
[HTML][HTML] Bias in artificial intelligence algorithms and recommendations for mitigation
LH Nazer, R Zatarah, S Waldrip, JXC Ke… - PLOS Digital …, 2023 - journals.plos.org
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …
algorithms may be shaped by various factors such as social determinants of health that can …