L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI)
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911530789896192 |
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| author | Shailendra, Samar Kadel, Rajan Sharma, Aakanksha Tahidul, Islam Mohammad Saxena, Urvashi Rahul |
| author_facet | Shailendra, Samar Kadel, Rajan Sharma, Aakanksha Tahidul, Islam Mohammad Saxena, Urvashi Rahul |
| contents | The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provides a rigorous foundation for evidence synthesis, yet the manual processes of data extraction and literature screening remain time-consuming and restrictive. Recent advances in Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), offer opportunities to automate and scale these tasks, thereby improving time and efficiency. However, reproducibility, transparency, and auditability, the core PRISMA principles, are being challenged by the inherent non-determinism of LLMs and the risks of hallucination and bias amplification. To address these limitations, this study integrates human-led synthesis with a GenAI-assisted statistical pre-screening step. Human oversight ensures scientific validity and transparency, while the deterministic nature of the statistical layer enhances reproducibility. The proposed approach systematically enhances PRISMA guidelines, providing a responsible pathway for incorporating GenAI into systematic review workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19236 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI) Shailendra, Samar Kadel, Rajan Sharma, Aakanksha Tahidul, Islam Mohammad Saxena, Urvashi Rahul Digital Libraries Artificial Intelligence Information Retrieval The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provides a rigorous foundation for evidence synthesis, yet the manual processes of data extraction and literature screening remain time-consuming and restrictive. Recent advances in Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), offer opportunities to automate and scale these tasks, thereby improving time and efficiency. However, reproducibility, transparency, and auditability, the core PRISMA principles, are being challenged by the inherent non-determinism of LLMs and the risks of hallucination and bias amplification. To address these limitations, this study integrates human-led synthesis with a GenAI-assisted statistical pre-screening step. Human oversight ensures scientific validity and transparency, while the deterministic nature of the statistical layer enhances reproducibility. The proposed approach systematically enhances PRISMA guidelines, providing a responsible pathway for incorporating GenAI into systematic review workflows. |
| title | L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI) |
| topic | Digital Libraries Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2603.19236 |