USE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE

MEDICAL LITERATURE ANALYSIS WITH SEMANTICASE

In support of our belief that the sector of Health can be largely supported by a controlled use of AI, a scientific article has been published that analyzes how the vastness of medical literature can be more easily managed through artificial intelligence tools.

THE ARTICLE

Santoro, M., & Nardini, C. (2025).

Institute for Applications of Computing, National Research Council

Large-Scale Analysis of the Medical Discourse on Rheumatoid Arthritis: Complementing with AI a Socio-Anthropologic Analysis. J, 8(4), 45.

DOI:10.3390/j8040045

Extended :

Medical discourse involves analyzing the far from impartial ways in which hypotheses and findings are presented in the dissemination of scientific publications. This places varying emphasis on context, relevance, robustness, and the assumptions that the public takes for granted. This concept is widely studied in socioanthropology. However, it remains generally neglected within the scientific community conducting research. Yet, analyzing discourse is crucial for several reasons: to shape policies that take into account a wide range of medical opportunities; to avoid overlooking promising but less traveled paths; to understand different types of representations of diseases, therapies, patients, and other stakeholders; and to understand how these terms are shaped by time and culture. While socioanthropologists traditionally use manual curation methods, limited by the length of the process, machine learning and artificial intelligence can offer complementary tools for exploring the vastness of an ever-growing medical literature. In this work, we propose a pipeline for analyzing medical discourse on therapeutic approaches to rheumatoid arthritis using topic modeling and transformer-based emotion and sentiment analysis, overall offering insights complementary to previous curation.

THE ROLE OF SEMANTICASE

The analysis described in the article was based on the use of semantic case, whose essential role has been clearly highlighted in the introduction and in the additional, S1.pdf., in which it is emphasized that semantic case represents a significant advance in the field of data processing natural language and semantic analysis.

Specifically designed to facilitate deep semantic exploration of textual data, semantic case It can meet the different needs of researchers and scientific analysts in different disciplines, and specifically also in the medical discipline.

The main analytical engine of Semanticase is in fact based on a custom implementation of the Structural Topic Model, which transcends the limitations of traditional topic modeling techniques by identifying thematic clusters within textual data, and uncovering the underlying hierarchical structure of these topics.

Using this advanced topic modeling technique, semantic case It allows researchers to delineate the dominant themes present in their corpus and to clarify the complex interactions and relationships between them, facilitating a significantly fuller exploration of the semantic richness inherent in the textual data examined.