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AI for all: bridging data gaps in machine learning and health.
Transl Behav Med
January 2025
Slone Epidemiology Center at Boston University, 72 E Concord St, Boston, MA, USA.
Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities.
View Article and Find Full Text PDFReimagining André Rey's method for recording the copy process of the Rey Complex Figure Test: A commentary.
J Neuropsychol
January 2025
Department of Aged Care and Rehabilitation, Liverpool Hospital, Sydney, New South Wales, Australia.
In 1941, André Rey published the Rey Complex Figure, a widely used test for assessing visual-constructional ability and visual memory. It consists of two parts: copy and recall. Evaluating the copy portion presents challenges, as it requires the administrator to focus on both the process and outcome.
View Article and Find Full Text PDFUnraveling the Invisible: Topological Data Analysis as the New Frontier in Radiology's Diagnostic Arsenal.
Tomography
January 2025
Department of Radiology, University of Padova, 35127 Padova, Italy.
This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods.
View Article and Find Full Text PDFAn analysis of the methodology used by the authors of the commented article is presented and errors related to data preparation are pointed out.
View Article and Find Full Text PDFDirected Acyclic Graphs in Decision-Analytic Modeling: Bridging Causal Inference and Effective Model Design in Medical Decision Making.
Med Decis Making
January 2025
Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Our commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.
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