Role of Deep Learning in Enhancing Medical Diagnosis Accuracy in Argentina

Patricia Elena ()

International Journal of Technology and Systems, 2025, vol. 10, issue 1, 69 - 81

Abstract: Purpose: To aim of the study was to analyze the role of deep learning in enhancing medical diagnosis accuracy in Argentina. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Deep learning has significantly enhanced medical diagnosis accuracy in Argentina, particularly in cardiology, radiology, and infectious disease detection. AI-driven echocardiograms have achieved high accuracy rates, reducing diagnostic errors and improving early disease detection. AI-powered models for COVID-19 diagnosis have streamlined response times and resource allocation. In cancer detection, AI has improved early tumor identification, but challenges remain due to biases in medical imaging datasets. Machine learning applications in diabetes risk assessment have proven effective in predicting high-risk patients. Despite these advancements, issues such as AI biases, limited dataset diversity, and challenges in integrating AI into healthcare systems persist. Unique Contribution to Theory, Practice and Policy: Artificial neural network (ANN) theory, computational learning theory (CLT) & pattern recognition theory may be used to anchor future studies on the role of deep learning in enhancing medical diagnosis accuracy in Argentina. Hospitals and diagnostic centers should develop AI-assisted workflows that seamlessly integrate deep learning models into radiology, pathology, and general diagnostics. Governments should encourage privacy-preserving AI techniques like differential privacy and federated learning to minimize data-sharing risks.

Date: 2025
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