Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vedula, Jagadeesh; Kakani, Tapankumar A.; Gupta, Rahul; Mohammed, Muneeruddin; Hudani, Karim; Logeshwaran, J. Title A name given to the resource Optimizing Disease Diagnosis and Treatment Through AI and Deep Learning Algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.98-102 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICV64824.2025.11085913" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085913</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013074156?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013074156?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vedula J., Digital Transformation Lead Meghaz Inc., Houston, TX, United States; Kakani T.A., Pactiv Evergreen Inc., Department of IT, Mundelein, IL, United States; Gupta R., Tejankar Hospital, M.P., Ujjain, India; Mohammed M., Eli Lilly, Indianapolis, IN, United States; Hudani K., Independent Researcher, Tampa, FL, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bangluru, 560029, India Description An account of the resource A Primer for Cancer Center Leaders Session 2 Natural Language Processing for Biomedical Text Medical data is not only numeric but also composed of unstructured text. These algorithms listen to various medical imaging, genomic data, and electronic health records to find correlations that can predict different diseases. Using convolutional neural networks to analyze images and recurrent neural networks to process sequential data, AI systems improve diagnostic accuracy and minimize the risk of human error. Additionally, deep learning algorithms targets patient-oriented drug administration by predicting therapeutic responses of individual patients, enhancing treatment response. Incorporating AI into clinical workflows allows us to synthesize vast datasets in real-time, provide clinicians with action items, and advocate for evidence-based medicine. However, problems including data privacy, model interpretability, and the need for large, annotated datasets continue. Such solutions in the form of explainable AI and deep learning would play an integral role in promoting the usage of these technologies over a longer duration in the medical ecosystem. This work shows how AI and deep learning can open avenues that may fundamentally change disease detection and treatments, leading to improved diagnosis and treatments tailored to the individual patient. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Deep Learning; Disease Diagnosis; ML; Natural Language Processing; Neural Networks Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151175-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online