The rapid expansion of scientific publications across disciplines has made traditional literature review methodologies increasingly difficult to execute efficiently. Researchers must analyze thousands of articles, identify emerging trends, synthesize evidence, detect research gaps, and evaluate methodological quality within limited timeframes. Generative Artificial Intelligence (Generative AI), powered by large language models, transformer architectures, retrieval-augmented generation, and intelligent knowledge representation techniques, has emerged as a transformative solution for automated scientific literature analysis. Unlike conventional text mining approaches that primarily perform keyword matching or statistical extraction, Generative AI demonstrates contextual understanding, semantic reasoning, automated summarization, question answering, citation synthesis, hypothesis generation, and research trend identification. These capabilities significantly improve the efficiency, scalability, and quality of scientific knowledge management while reducing researcher workload.
This review systematically examines recent developments in Generative AI for automated scientific literature analysis by synthesizing evidence from the provided contemporary literature covering artificial intelligence, workflow automation, cloud intelligence, cybersecurity, financial AI, process mining, enterprise automation, reinforcement learning, digital transformation, and intelligent computing infrastructures. The review develops a comprehensive analytical framework describing the complete literature-analysis pipeline, including literature acquisition, document preprocessing, semantic embedding, knowledge extraction, contextual reasoning, automated synthesis, evidence validation, and research recommendation generation. Furthermore, the study critically evaluates technological enablers such as transformer-based architectures, cloud-edge computing infrastructures, retrieval-augmented generation, agentic AI, workflow automation, and scalable enterprise AI systems that collectively support intelligent literature analysis (Krishnan & Bhat, 2025; Kumar, 2025; Venkiteela, 2026).
The review identifies significant opportunities in accelerating systematic reviews, improving interdisciplinary knowledge discovery, reducing information overload, supporting evidence-based decision making, and enabling continuous scientific monitoring. Simultaneously, important challenges remain concerning hallucination, citation reliability, explainability, reproducibility, privacy, governance, computational scalability, and ethical deployment. The findings suggest that future literature analysis platforms will increasingly integrate Generative AI with retrieval systems, human-in-the-loop verification, autonomous research agents, and standardized governance frameworks to produce trustworthy, scalable, and transparent scientific intelligence. This review contributes a structured conceptual framework that integrates recent advances in Generative AI with automated scientific literature analysis while identifying future research opportunities for developing reliable AI-assisted scientific discovery ecosystems.

Generative Artificial Intelligence, Scientific Literature Analysis, Literature Review Automation, Large Language Models

G. Krishnan and A. K. Bhat, "Empower Financial Workflows: Hyper Automation Framework Utilizing Generative Artificial Intelligence and Process Mining," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2025, pp. 2041-2047, doi: 10.1109/ICoICI65217.2025.11254280.

Deshpande, A. a. P. S. (2025). Real-Time encryption and secure communication for sensor data in autonomous systems. Journal of Information Systems Engineering & Management, 10(41s), 41–55. https://doi.org/10.52783/jisem.v10i41s.7585

Modadugu, J. K., Venkata, R. T. P., & Venkata, K. P. (2025b). Philip, P. G. (2026). AI-Based Energy Optimization in Smart Buildings with Renewable Energy Integration: A Construction Project Management Perspective. The American Journal of Engineering and Technology, 8(06), 26–37. https://doi.org/10.37547/tajet/Volume08Issue06-01: Integrating AI and data processing in loan platforms. International Journal of Innovative Research and Scientific Studies, 8(6), 400–409. https://doi.org/10.53894/ijirss.v8i6.9617

Kale, A. (2025). FX Hedging Algorithms for Crypto-Native Companies. International Journal of Advanced Artificial Intelligence Research, 2(10), 09-14. https://doi.org/10.55640/ijaair-v02i10-02

Kishore Bandela . (2025). Use Of Recycled Plastic In Asphalt Mixtures For Road Construction. (2025). International Journal of Environmental Sciences, 720-739.

Hari Dasari. (2025). Implementing Site Reliability Engineering (SRE) in Legacy Retail Infrastructure. The American Journal of Engineering and Technology, 7(07), 167–179. https://doi.org/10.37547/tajet/Volume07Issue07-16

Karim, A. S. A. (2025). MITIGATING ELECTROMAGNETIC INTERFERENCE IN 10G AUTOMOTIVE ETHERNET: HYPERLYNX-VALIDATED SHIELDING FOR CAMERA PCB DESIGN IN ADAS LIGHTING CONTROL. International Journal of Apllied Mathematics, 38(2s), 1257–1268. https://doi.org/10.12732/ijam.v38i2s.718

Valiveti, S. S. S. (2025). AI-Driven Behavioral Biometrics for 401(k) Account Security. International Research Journal of Advanced Engineering and Technology, 23-26. https://doi.org/10.55640/irjaet-v02i06-04

M. A. Hussain, V. B. Meruga, A. K. Rajamandrapu, S. R. Varanasi, S. S. S. Valiveti and A. G. Mohapatra, "Generative AI Sensor Fusion for Secure Digital Twin Ecosystems: A Standardization-Aligned Framework for Cyber-Physical Systems," in IEEE Communications Standards Magazine, doi: 10.1109/MCOMSTD.2026.3660106.

Carolina, I. R. &. I. M. D. N., USA, & Tiwari, S. K. (2025b). Automation Driven Digital Transformation Blueprint: Migrating legacy QA to AI augmented pipelines. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(12), 01–20. https://doi.org/10.64917/feaiml/volume02issue12-01

V. S. N. Kanikanti, S. K. Tiwari, V. Nayan, S. Suryawanshi, V. Banerjee and I. E. Ahmed, "Deep Q-Learning Driven Dynamic Optimal Task Scheduling for Cloud Computing Using Optimal Queuing," 2025 10th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates, 2025, pp. 112-117, doi: 10.1109/ITT69610.2025.11352940.

Kathi, S. R. (2025). Enterprise-Grade CI/CD pipelines for mixed Java version environments using Jenkins in Non-Containerized environments. Journal of Engineering Research and Sciences, 4(9), 12. https://doi.org/10.55708/js0409002

Dasari, H. (2025). SITE RELIABILITY ENGINEERING PRACTICES FOR ERROR BUDGET MANAGEMENT IN LARGE-SCALE SYSTEMS. International Journal of Apllied Mathematics, 38(5s), 991–1001. https://doi.org/10.12732/ijam.v38i5s.366

Hebbar, K. S. (2025). Priority-Aware reactive APIs: Leveraging Spring WebFlux for SLA-Tiered traffic in financial services. European Journal of Electrical Engineering and Computer Science, 9(5), 31–40. https://doi.org/10.24018/ejece.2025.9.5.743

Gangula, S. (2026). Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices. The American Journal of Engineering and Technology, 8(01), 07–19. https://doi.org/10.37547/tajet/Volume08Issue01-02

Karim, A. S. A. (2025). MITIGATING ELECTROMAGNETIC INTERFERENCE IN 10G AUTOMOTIVE ETHERNET: HYPERLYNX-VALIDATED SHIELDING FOR CAMERA PCB DESIGN IN ADAS LIGHTING CONTROL. International Journal of Apllied Mathematics, 38(2s), 1257–1268. https://doi.org/10.12732/ijam.v38i2s.718

Abdul Salam Abdul Karim. (2023). Fault-Tolerant Dual-Core Lockstep Architecture for Automotive Zonal Controllers Using NXP S32G Processors. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 877–885. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7749

H. K. Krishnamurthy Sukumar, "A Novel Hybrid Grey Wolf Whale Optimization for Effectual Job Scheduling and Resource Distribution in Dynamic Cloud Computing," 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), Nagpur, India, 2025, pp. 1-6, doi: 10.1109/ICSIT65336.2025.11293898.

A. K. Bhat and G. Krishnan, "A Review of Artificial Intelligence in Finance: Driving the Next Wave of Industry Innovation," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2025, pp. 2033-2040, doi: 10.1109/ICoICI65217.2025.11252894.

Sayyed, Z. (2025). Application Level Scalable Leader Selection Algorithm for Distributed Systems. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3856

K. S. Hebbar, “Priority-Aware Reactive APIs: Leveraging Spring WebFlux for SLA-Tiered Traffic in Financial Services,” European Journal of Electrical Engineering and Computer Science, vol. 9, no. 5, pp. 31–40, Sep. 2025, doi: 10.24018/ejece.2025.9.5.743.

Shruti Worlikar 2025. Real-Time Patient Monitoring and Alerting in Hospitals Using AWS Lake House Architecture. Frontiers in Emerging Computer Science and Information Technology. 2, 08 (Aug. 2025), 07–14. DOI:https://doi.org/10.37547/fecsit/Volume02Issue08-02.

Shounik, S. (2025). Redefining Entry-Level Analyst Roles in M&A: Essential Skillsets in the Age of AI-Powered Diligence. The American Journal of Applied Sciences, 7(07), 101–110. https://doi.org/10.37547/tajas/Volume07Issue07-11

Chakravartula, K. N. (2025). Optimizing the loan origination system using CRM to improve the agri business workflows. Journal of Information Systems Engineering & Management, 10(36s), 287–294. https://doi.org/10.52783/jisem.v10i36s.6413

Sagar Kesarpu. (2025). Chaos Engineering as a Learning Framework: A Human-Centered Model for Developing High-Reliability Engineering Teams. The American Journal of Engineering and Technology, 7(12), 57–64. https://doi.org/10.37547/tajet/Volume07Issue12-05

R. Laheri, "AI-Enhanced Biometric Systems for Insurance: Secure Authentication and Regulatory Compliance," 2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), Chennai, India, 2025, pp. 1-6, doi: 10.1109/ICECONF65644.2025.11379513.

Singh, J. (2024). The impact of real-time analytics dashboards on decision-making quality and organizational responsiveness: An empirical study. Journal of Information Systems Engineering and Management, 9(3).

Padmanabham Venkiteela (Decemeber 2025) n8n: An Open-Source Workflow Automation Platform for Enterprise Integration and AI-Driven Orchestration, International Journal of Computer Applications. https://doi.org/10.5120/ijca2025926031.

Venkiteela, P. (2026). An Enterprise Agentic Architecture Framework for Agentic AI Governance and Scalable Autonomy. Scientific Journal of Computer Science, 2(1), 1–17. https://doi.org/10.64539/sjcs.v2i1.2026.368.

Thanvi, Y. S. (2026). A Longitudinal Review on the State of Cybersecurity 2022-2025: Workforce, Governance, Risk, and Operational Maturity, and findings from ISACA Global Surveys. American Journal of Technology, 5(2), 39–51. https://doi.org/10.58425/ajt.v5i2.486.

Singh, V. (2024). The impact of artificial intelligence on compliance and regulatory reporting. J. Electrical Systems, 20, 4322-4328.

Fnu, H., Mirza, M.H., Marri, M.R. et al. Blockchain-Assisted Transformer CNN Framework with Optimal Feature Selection for Real-Time Digital Payment Fraud Detection. Int J Comput Intell Syst 19, 70 (2026). https://doi.org/10.1007/s44196-025-01126-6.

Goyal, K., Mirza, M. H., Nutalapati, P., Chawra, V. S., & Mulla, F. M. (2026). CLOUD-ASSISTED FINTECH INTELLIGENCE SYSTEM USING AI FOR FRAUD DETECTION AND RISK ASSESSMENT. Scientific Culture, 12(1, Part 1), 4603.

Kaur, K. 2026. Augmented Business Intelligence for Predictive Customer Segmentation. Frontiers in Business Innovations and Management. 3, 01 (Jan. 2026), 01–14. DOI:https://doi.org/10.64917/fbim/Volume03Issue01-01.

Sravan Kumar Nidiganti. (2025). Robotic Process Automation in Pharmacy Benefit Manager (PBM) Quality. The American Journal of Applied Sciences, 7(07), 93–100. https://doi.org/10.37547/tajas/Volume07Issue07-10.

Pai, D. (2025). Prompt Engineering Frameworks for Generative AI in Credit Analysis. https://doi.org/10.52783/jisem.v10i45s.9035.

Pandey, C. P., Upadhyay, H., Kale, A., Joshi, P., & Sri, B. (2026). AI-driven fraud detection and risk forecasting framework for real-time financial transactions. Scientific Culture, 12(1 Part 1), 3425.

Kathi, S.R., Joshi, P., Muralidhar, V. and Venugopalan, A., 2026. Secure migration patterns from Java 8 to Java 17 in the mission-critical ecosystem: a risk-driven approach to modernization. Future Technology, 5(2), pp.297-309. DOI: 10.55670/fpll.futech.5.2.27.

SinghJatav, D., Amin, M. M., Kodela, S., Nayan, V., Wannous, M., & Khalifa, G. S. (2025, November). Hybrid Reinforcement and Deep Learning Model for Payment Delay Optimization in Supply Chain Finance. In 2025 10th International Conference on Information Technology Trends (ITT) (pp. 170-175). IEEE.

Philip, P. G. (2025). Predictive Maintenance Approach for Electric Power Systems Using Machine Learning. The American Journal of Interdisciplinary Innovations and Research, 7(09), 145–160. Retrieved from https://theamericanjournals.com/index.php/tajiir/article/view/ml-predictive-maintenance-power-systems

K. Kumar, "Edge-to-Cloud AI Inference Pipelines: A Resilient Architecture for Real-Time Decision Systems," 2025 International Conference on Computer and Applications (ICCA), Bahrain, Bahrain, 2025, pp. 1-6, doi: 10.1109/ICCA66035.2025.11430905.