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| http://dx.doi.org/10.3389/fphys.2023.1267632 | DOI Listing |
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Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment.
Front Oncol
December 2024
Radiotherapy Department, Montpellier Regional Cancer Institute, Montpellier, France.
Introduction: Following a preliminary work validating the technological feasibility of an adaptive workflow with Ethos for whole-breast cancer, this study aims to clinically evaluate the automatic segmentation generated by Ethos.
Material And Methods: Twenty patients initially treated on a TrueBeam accelerator for different breast cancer indications (right/left, lumpectomy/mastectomy) were replanned using the Ethos emulator. The adaptive workflow was performed using 5 randomly selected extended CBCTs per patient.
Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations.
J Pathol Inform
January 2025
U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, United States of America.
Objective: With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).
Materials And Methods: We obtained clinical metadata for hematoxylin and eosin-stained glass slides and corresponding scanned whole slide images (WSIs) of TNBC core biopsies from two US academic medical centers.
Artificial intelligence, existential risk and equity: the need for multigenerational bioethics.
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Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
Editorial: Genetic and cellular heterogeneity in tumors.
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Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Navigating the unseen peril: safeguarding medical imaging in the age of AI.
Front Artif Intell
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Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
In response to the increasing significance of artificial intelligence (AI) in healthcare, there has been increased attention - including a Presidential executive order to create an AI Safety Institute - to the potential threats posed by AI. While much attention has been given to the conventional risks AI poses to cybersecurity, and critical infrastructure, here we provide an overview of some unique challenges of AI for the medical community. Above and beyond obvious concerns about vetting algorithms that impact patient care, there are additional subtle yet equally important things to consider: the potential harm AI poses to its own integrity and the broader medical information ecosystem.
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