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Adaptive Precision Medicine: AI-Driven Real-Time Treatment Customization Using Genomic and Clinical Data
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| Author:
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V.RAVIKUMAR, P. BALAKUMAR, R. DHANALAKSHMI
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| Abstract:
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Artificial intelligence (AI) enabled adaptive precision medicine utilizes genomic and clinical data to generate real-time personalized treatment strategies. This study presents an artificial intelligence (AI)-driven real-time adaptive precision medicine (ART-PM) framework using a multi-modal AI to dynamically optimize on treatment, the XAI to provide more insight into the clinical decision, and establishes a secure, decentralized data processing-performed through federated learning. In the framework, the genomic sequencing, electronic health records, and real-time measurements from the patient monitor are integrated to build a personalized digital twin model and constantly grow the treatment strategy by improving the patient's response and AI-fueled predictive analytics. To predict drug efficacy and minimize the drug adverse effects, as well as to optimize treatment pathways, we exploit a hybrid approach of deep learning and reinforcement learning. Blockchain enhanced security and federated learning guarantees data privacy in the system while multiple institutions can still collaborate without jeopardising the confidentiality of patients. Real world case studies are also used to validate performance from which it is shown to achieve improvements in predictive accuracy (as opposed to traditional precision medicine models). The proposed scheme provides an ART-PM framework to overcome one of the key challenges in AI-driven healthcare, such as ethical issues, interpretability, and scalability, which makes it an innovative solution to personalized medicine. The work reported here helps to move AI-enabled precision medicine forward to what will be more effective, transparent and adaptive treatment paradigms in real world clinical use.
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| Keyword:
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AI-Driven, predictive, treatment, Customization
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.01.420
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| Download:
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