From data extraction to analysis: A comparative study of ELISE capabilities in scientific literature

Maxime GOBIN^*Muriel GosnatSeindé ToureLina FaikJoel BelafaAntoine Villedieu De TorcyFlorence Armstrong

• Biolevate, Paris, France

The exponential growth of scientific literature presents challenges for pharmaceutical, biotechnological, and Medtech industries, particularly in regulatory documentation, clinical research, and systematic reviews. Ensuring accurate data extraction, literature synthesis, and compliance with industry standards require AI tools that not only streamline workflows but also uphold scientific rigor. This study evaluates the performance of AI tools designed for bibliographic review, data extraction, and scientific synthesis, assessing their impact on decision-making, regulatory compliance, and research productivity. The AI tools assessed include general-purpose models like ChatGPT and specialized solutions such as ELISE (Elevated LIfe SciencEs), SciSpace/Typeset, Humata, and Epsilon.The evaluation is based on three main criteria: Extraction, Comprehension, and Analysis with Compliance and Traceability (ECACT) as additional dimensions. Human experts established reference benchmarks, while AI Evaluator models ensure objective performance measurement. The study introduces the ECACT score, a structured metric assessing AI reliability in scientific literature analysis, regulatory reporting and clinical documentation.Results demonstrate that ELISE consistently outperforms other AI tools, excelling in precise data extraction, deep contextual comprehension, and advanced content analysis. ELISE's ability to generate traceable, well-reasoned insights makes it particularly well-suited for high-stakes applications such as regulatory affairs, clinical trials, and medical documentation, where accuracy, transparency, and compliance are paramount. Unlike other AI tools, ELISE provides expert-level reasoning and explainability, ensuring AI-generated insights align with industry best practices. ChatGPT is efficient in data retrieval but lacks precision in complex analysis, limiting its use in highstakes decision-making. Epsilon, Humata, and SciSpace/Typeset exhibit moderate performance, with variability affecting their reliability in critical applications.In conclusion, while AI tools such as ELISE enhance literature review, regulatory writing, and clinical data interpretation, human oversight remains essential to validate AI outputs and ensure compliance with scientific and regulatory standards. For pharmaceutical, biotechnological, and Medtech industries, AI integration must strike a balance between automation and expert supervision to maintain data integrity, transparency, and regulatory adherence.