AI-Powered Auxiliary Medical Diagnostic Systems

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dc.contributor.authorFarias, Mylène C. Q.
dc.date.accessioned2024-04-11T19:31:40Z
dc.date.available2024-04-11T19:31:40Z
dc.date.issued2024-03
dc.description.abstractDeep Learning models are being used to analyze medical data and, most specifically, medical images, and to identify patterns and abnormalities that may not be (YET) visible to radiologists and physicians in general. These auxiliary diagnostic systems allow for an early detection of chronic diseases, such as heart conditions and cancer. AI models can process large amounts of data quickly and accurately. They can also be used to track health data over time and identify suspicious changes. Finally, AI models can be used to identify rare diseases and conditions that are difficult for humans to diagnose. But the area still faces several challenges: Availability of balanced datasets; Assurance of accuracy and reliability; Explainability; Privacy and security; Robustness to diversity in formats, degradations, etc.
dc.description.departmentComputer Science
dc.formatImage
dc.format.extent1 page
dc.format.medium1 file (.pdf)
dc.identifier.citationFarias, M. C. Q. (2024). AI-powered auxiliary medical diagnostic systems. Poster presented at the Health Scholar Showcase, Translational Health Research Center, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/18447
dc.language.isoen
dc.sourceHealth Scholar Showcase, 2024, Texas State University Translational Health Science Center, San Marcos, Texas, United States.
dc.subjectAI
dc.subjectdiagnostic
dc.subjectauxiliary
dc.subjectmedical
dc.titleAI-Powered Auxiliary Medical Diagnostic Systems
dc.typePoster

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