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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 PDFWhen machines start examining tissue specimens on their own.
Neuro Oncol
January 2025
Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871 Japan.
Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward.
Animals (Basel)
January 2025
Zinpro Corporation, Eden-Prairie, MN 55344, USA.
Standards for data generation and collection are important for integration and for achieving data-driven actionable insights in dairy farming. Data integration and analysis are critical for advancing the dairy industry, enabling better decision-making, and improving operational efficiencies. This commentary paper discusses the challenges of and proposes pathways for standardizing data generation and collection based on insights from a multidisciplinary group of stakeholders.
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 PDFTransforming Healthcare: Artificial Intelligence (AI) Applications in Medical Imaging and Drug Response Prediction.
Genome Integr
January 2025
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability.
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