Publications by authors named "R Mazurchuk"
Article Synopsis
- Innovation in medical imaging using AI and machine learning requires thorough data collection and algorithm improvements, along with careful evaluation of factors like bias and trustworthiness.
- Successfully integrating AI/ML into clinical settings is challenging and hinges on addressing issues in model design, development, regulatory compliance, and stakeholder collaboration.
- Tackling these complexities is essential not only for overcoming current obstacles but also for unlocking new opportunities in the field of radiology.
The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples.
View Article and Find Full Text PDFRapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods.
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- - The National Cancer Institute held a think-tank meeting to gather expert insights on using multiomic single-cell analyses, particularly single-cell proteomics, to create advanced cancer biomarkers for risk assessment, early detection, diagnosis, and treatment targets.
- - The discussion covered challenges in single-cell analysis, including methods for analyzing cells from different tissue states, detecting secreted molecules, identifying new cell types, and integrating multiple types of data effectively.
- - Experts also explored technical improvements needed for single-cell proteomics, including enhancing measurement sensitivity, achieving adequate data coverage, and effectively visualizing complex data sets to better understand intercellular communication in cancerous tissues.
Many cancers evolve from benign precancerous lesions and have a natural history of progression that provides a window of opportunity for intervention. The biological mechanisms underlying this evolutionary trajectory can only be truly understood through an extensive characterization of the molecular, cellular, and non-cellular properties of premalignant and malignant tumors, and must also recognize how the microenvironment (stromal cells, immune cells, and other types of cells) contributes to this evolution. We describe here the need to develop comprehensive molecular and cellular atlases for organ-specific premalignant lesions while capturing the spatial, structural, and functional changes over time that will provide a greater understanding of how premalignancy transitions to malignancy.
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