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[Online]The Role of Intelligent Computing in Medical and Genomic Healthcare Innovations
The Role of Intelligent Computing in Medical and Genomic Healthcare Innovations
ID：173
Submission ID：523 View Protection：ATTENDEE
Updated Time：2025-12-23 13:37:44 Hits：309
Online
Start Time：2025-12-30 15:15 （Asia/Amman）
Duration：15min
Session：[S7] Track 7: Pattern Recognition, Computer Vision and Image Processing » [S7-2] Track 7: Pattern Recognition, Computer Vision and Image Processing
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Abstract
Intelligent computing has revolutionized the landscape of medical and genomic healthcare by enabling data-driven innovations that enhance diagnostic accuracy and treatment efficacy. With the advent of AI technologies, healthcare is shifting towards more personalized, predictive, and precise medical interventions. However, traditional treatment planning approaches often fail to account for the complexities of genomic variability and individual health profiles, leading to suboptimal outcomes and generalized therapeutic strategies. These methods lack adaptability, are heavily rule-based, and do not efficiently utilize the vast biomedical data available. To address these challenges, we propose a novel framework Personalized Treatment Planning using Deep Learning Algorithm (PTP-DLA). This method integrates patient-specific clinical and genomic data through advanced deep learning architectures, including Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for temporal pattern recognition. The framework employs data fusion techniques and attention mechanisms to dynamically tailor treatment plans that are both adaptive and individualized. The PTP-DLA system is designed to support clinicians by recommending optimized treatment regimens based on predicted outcomes, patient history, and genomic markers. It can be applied across various medical conditions, including oncology, rare genetic disorders, and chronic diseases, thereby enhancing decision-making in complex clinical scenarios. Experimental evaluations indicate that PTP-DLA significantly outperforms existing models in terms of treatment outcome prediction accuracy, patient-specific adaptability, and overall computational efficiency. These findings suggest that the proposed method holds substantial promise in bridging the gap between genomic data analysis and actionable, personalized healthcare delivery.
Keywords
Intelligent Computing, Personalized Treatment Planning, Deep Learning Algorithm, Genomic Healthcare, Precision Medicine, Cnn, Rnn, Data Fusion, Clinical Decision Support, Medical Ai.
Speaker
Prof Anil Srivastava
Professor Assistant Professor； Savitribai Phule Pune University； Department of BBA/BBA(CA)Submission Author
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