Transforming Precision Medicine through Artificial Intelligence
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I4P106Keywords:
Precision Medicine, Individualized Care, Patient-Specific Data, Clinical Data Integration, MultiOmics Analytics, Lifestyle And Digital Biomarkers, Disease Stratification, Risk Prediction Models, Disease Progression Modeling, Cohort Assembly, Electronic Health Records Analytics, Artificial Intelligence In Healthcare, Transdisciplinary Methods, Targeted Therapeutics, Clinical Decision Support, DataDriven Drug Development, Oncology And Rare Diseases, Cardio Metabolic And Infectious Diseases, Neuropsychiatric Disorders, Personalized Treatment PathwaysAbstract
Precision medicine seeks to provide individualized information-based care across a range of therapeutic areas, utilizing patient-specific clinical, biological, and lifestyle data. The clinical implementation of precision medicine remains nascent but has the potential to facilitate the discovery, development, and delivery of therapeutics that target disease subtypes and patient populations defined by their unique characteristics. It offers new opportunities for treatment at any stage of disease, from prevention in high-risk groups to rethinking indications for established products. Three interconnected developments enable the effective implementation of precision medicine: the creation of large and diverse biological, clinical, imaging, digital, and lifestyle datasets; the emergence of new transdisciplinary methods to derive knowledge from these datasets; and the establishment of new product development models that leverage the acquired knowledge to deliver more targeted, safer, and more efficacious therapeutics. The application of artificial intelligence (AI) to clinical, imaging, and lifestyle data, as well as new approaches to risk prediction and disease progression modeling, cohort assembly, and knowledge extraction from electronic health records are enabling more accurate stratification of complex diseases within oncology, rare diseases, cardio-metabolic conditions, infectious diseases, and neuropsychiatric disorders.
References
[1] Pamisetty, V. (2021). A cloud-integrated framework for efficient government financial management and unclaimed asset recovery. Available at SSRN.
[2] Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K., & SPIRIT-AI and CONSORT-AI Working Group. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence. Nature Medicine, 26, 1364–1374.
[3] Lakkarasu, P., & Kalisetty, S. (2022). Hybrid Cloud and AI Integration for Scalable Data Engineering: Innovations in Enterprise AI Infrastructure. Math. Stat. Eng. Appl., 71(4), 16774-16784.
[4] Inala, R. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280-304.
[5] Vankayalapati, R. K., Pandugula, C., Ganti, V. K. A. T., & Mishra, G. (2022). AI-powered self-healing cloud infrastructures: A paradigm for autonomous fault recovery. Migration Letters, 19(6), 1173-1187.
[6] Srinivas Kalisetty (2020). Intelligent Supply Chain Ecosystems: Cloud-Native Architectures and Big Data Integration in Retail and Manufacturing Operations. Open Journal of Educational Research, 1(1), 1-19. https://doi.org/10.31586/ojer.2020.1343
[7] Mashetty, S. Data-driven insights for community investment through mortgage-backed securities. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[8] Paleti, S. (2022). Financial Innovation through AI and Data Engineering: Rethinking Risk and Compliance in the Banking Industry. Available at SSRN.
[9] Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
[10] Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
[11] Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
[12] Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
[13] Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
[14] Puli, V. O. R., & Maguluri, K. K. (2022). Deep learning applications in materials management for pharmaceutical supply chains. Migration Letters, 19(6), 1144-1158.
[15] Chakilam, C., Suura, S. R., Koppolu, H. K. R., & Recharla, M. (2022). From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development. Journal of Survey in Fisheries Sciences. https://doi. org/10.53555/sfs. v9i3, 3619.
[16] Reddy, V. A. R. (2022). Designing Fault-Tolerant Data Ingestion Pipelines for High-Volume Healthcare Transactions. Frontiers in Health Informatics, 11, 861-889.
[17] Nandan, B. P. (2022). AI-Powered Fault Detection In Semiconductor Fabrication: A Data-Centric Perspective. Lebcir, I., Mageswari, SU, Bhosale, YH, Nagubandi, AR, & Mahabooba, MM Agile Strategic Management in the Age of Disruption: Leveraging AI and Data Analytics for Competitive Advantage.
[18] Adusupalli, B. (2021). Multi-Agent Advisory Networks: Redefining Insurance Consulting with Collaborative Agentic AI Systems. Journal of International Crisis and Risk Communication Research, 45-67.
[19] Pamisetty, A. (2022). A Comparative Study of AWS, Azure, and GCP for Scalable Big Data Solutions in Wholesale Product Distribution. International Journal of Scientific Research and Modern Technology, 71-88.
[20] Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
[21] Inala, R. (2020). Building Foundational Data Products for Financial Services: A MDM-Based Approach to Customer, and Product Data Integration. Universal Journal of Finance and Economics, 1(1), 1-18.
[22] Pamisetty, A. (2022). Big Data can Generate Major Opportunities for Manufacturing Supply Chains. International Journal of Scientific Research and Modern Technology, 1(12), 238-251.
[23] Maguluri, K. K., Yasmeen, Z., & Nampalli, R. C. R. (2022). Big Data Solutions For Mapping Genetic Markers Associated With Lifestyle Diseases. Migration Letters, 19(6), 1188-1204.
[24] Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
[25] Polineni, T. N. S., Pandugula, C., & Ganti, V. K. A. T. (2022). AI-driven automation in monitoring post-operative complications across health systems. Global Journal of Medical Case Reports, 2(1), 32-46.
[26] Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650.
[27] Challa, K., Burugulla, J. K. R., Pandiri, L., Pamisetty, V., & Paleti, S. (2022). Optimizing Digital Payment Ecosystems: Ai-Enabled Risk Management, Regulatory Compliance, And Innovation In Financial Services. Migration Letters, 19, 1748-1769.
[28] Mashetty, S. (2022). Innovations in mortgage-backed security analytics: A patent-based technology review. Available at SSRN 5238910.
[29] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
[30] Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
[31] Reddy, V. A. R. (2022). Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms. International Journal of Science, Research and Technology, 5(5), 8511-8521.
[32] Pamisetty, V. (2022). Transforming fiscal impact analysis with AI, big data, and cloud computing: A framework for modern public sector finance. Big Data, and Cloud Computing: A Framework for Modern Public Sector Finance (November 30, 2022).
[33] Recharla, M. (2020). Targeted Gene Therapy for Spinal Muscular Atrophy: Advances in Delivery Mechanisms and Clinical Outcomes. International Journal of Science and Research (IJSR), 9(12), 1921-1934.
[34] Botlagunta, P., & Chitta, S. (2022). Advanced optical proximity correction (OPC) techniques in computational lithography: Addressing the challenges of pattern fidelity and edge placement error. Glob. J. Med. Case Rep, 2, 58-75.
[35] Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020). Artificial intelligence versus clinicians: Systematic review of diagnostic accuracy. BMJ, 368, m689.
[36] Sriram, H. K., Adusupalli, B., Singreddy, S., & Malempati, M. (2021). Revolutionizing risk assessment and financial ecosystems with smart automation, secure digital solutions, and advanced analytical frameworks. Murali, Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks (December 27, 2021).
[37] Paleti, S. (2021). Cognitive core banking: A data-engineered, AI-infused architecture for proactive risk compliance management. AI-Infused Architecture for Proactive Risk Compliance Management (December 21, 2021).
[38] Kalisetty, S., & Kothpalli Sondinti, L. R. (2022). AI-Native Cloud Platforms: Redefining Scalability and Flexibility in Artificial Intelligence Workflows. Linguistic and Philosophical Investigations, 21(1), 1-15.
[39] Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
[40] Mashetty, S. (2022). Enhancing Financial Data Security And Business Resiliency In Housing Finance: Implementing AI-Powered Data Analytics, Deep Learning, And Cloud-Based Neural Networks For Cybersecurity And Risk Management. Migration Letters, 19(6), 1302-1818.
[41] Maguluri, K. K., & Ganti, V. K. A. T. (2019). Predictive Analytics in Biologics: Improving Production Outcomes Using Big Data.
[42] Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
[43] Sheelam, G. K., & Nandan, B. P. (2022). Integrating AI And Data Engineering For Intelligent Semiconductor Chip Design And Optimization. Migration Letters, 19, 2178-2207.
[44] Paleti, S. (2022). Fusion bank: Integrating AI-driven financial innovations with risk-aware data engineering in modern banking. Decision Making, 2326, 9865.
[45] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
[46] Pamisetty, A. (2022). Integrating Big Data, AI, and Financial Modeling in Cloud-Based Insurance and Banking Ecosystems. AI, and Financial Modeling in Cloud-Based Insurance and Banking Ecosystems (December 05, 2022).
[47] Pandugula, C., & Yasmeen, Z. (2019). A Comprehensive Study of Proactive Cybersecurity Models in Cloud-Driven Retail Technology Architectures. Universal Journal of Computer Sciences and Communications, 1(1), 1253.
[48] Inala, R. (2021). A New Paradigm in Retirement Solution Platforms: Leveraging Data Governance to Build AI-Ready Data Products. Journal of International Crisis and Risk Communication Research, 286-310.
[49] Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Journal of International Crisis and Risk Communication Research.
[50] Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
[51] Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
[52] Mattaparthi, R. (2021). Unified Data Lineage and Quality Governance Framework for Multi-Source Sensor Streams in Heavy-Duty Powertrain Manufacturing. Online Journal of Mechanical Engineering, 1(1), 1-15.
[53] Chava, K., Chakilam, C., & Recharla, M. (2021). Machine Learning Models for Early Disease Detection: A Big Data Approach to Personalized Healthcare. International Journal of Engineering and Computer Science, 10(12), 25709-25730.
[54] Adusupalli, B. (2021). Multi-Agent Advisory Networks: Redefining Insurance Consulting with Collaborative Agentic AI Systems. Journal of International Crisis and Risk Communication Research, 45-67.
[55] Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172.
[56] Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
