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Is there a mental health diagnostic crisis in primary care? Current research practices in global mental health cannot answer that question.
Epidemiol Psychiatr Sci
January 2025
Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, Center for Global Mental Health, King's College London, London, UK.
In low- and middle-income countries, fewer than 1 in 10 people with mental health conditions are estimated to be accurately diagnosed in primary care. This is despite more than 90 countries providing mental health training for primary healthcare workers in the past two decades. The lack of accurate diagnoses is a major bottleneck to reducing the global mental health treatment gap.
View Article and Find Full Text PDFThe ITP diagnostic challenge.
Br J Haematol
January 2025
Hematology Department, Hospital Universitario de Burgos, Burgos, Spain.
Despite numerous advances, the diagnosis of immune thrombocytopenia (ITP) remains a diagnosis of exclusion. This commentary analyses and contextualizes the recent report from Garabet et al. that MicroRNA-199a-5p could be used as a diagnostic marker for ITP.
View Article and Find Full Text PDFAI 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 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.
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