ItemConference Paper

Optimizing Disease Diagnosis and Treatment Through AI and Deep Learning Algorithms

Title
Optimizing Disease Diagnosis and Treatment Through AI and Deep Learning Algorithms
Creator
Vedula, Jagadeesh; Kakani, Tapankumar A.; Gupta, Rahul; Mohammed, Muneeruddin; Hudani, Karim; Logeshwaran, J.
Description
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.
Source
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.98-102
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Artificial Intelligence; Deep Learning; Disease Diagnosis; ML; Natural Language Processing; Neural Networks
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151175-3;
Format
online
Language
English
Type
Conference paper

Citation

Vedula, Jagadeesh; Kakani, Tapankumar A.; Gupta, Rahul; Mohammed, Muneeruddin; Hudani, Karim; Logeshwaran, J., “Optimizing Disease Diagnosis and Treatment Through AI and Deep Learning Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 21, 2026, https://archives.christuniversity.in/items/show/26032.

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