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Large Language Models: Theories, Methodologies and Real-World Applications in Healthcare
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Large Language Models: Theories, Methodologies and Real-World Applications in Healthcare

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Medical Informatics and Healthcare Engineering".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1481

Special Issue Editor

Dr. Flavio Bertini

E-Mail Website
Guest Editor
Department of Mathematical, Physical and Computer Sciences, University of Parma, 43124 Parma, Italy
Interests: big data; data analysis; health data analysis; data mining; information retrieval; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, large language models (LLMs) have undergone a remarkable evolution, transforming the landscape of natural language processing. As these models advanced, their ability to understand context, generate coherent text, and even perform reasoning tasks improved significantly, leading to their integration into various sectors of society. In particular, in the healthcare domain, LLMs hold immense promise. Their proficiency in understanding and generating human-like text enables them to analyse complex medical literature, stay updated on the latest research, and assist healthcare professionals in carrying out mass screening of high-impacting diseases. These models could aid in extracting valuable insights from patient records, suggesting potential diagnoses based on symptoms, and even assisting in the interpretation of medical data. This Special Issue invites submissions of scientific findings that showcase foundational theories, methodologies, real-world applications of LLMs in healthcare, and also future strategic plans.

Dr. Flavio Bertini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied System Innovation is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in healthcare
  • LLMs in healthcare
  • fine-tuning LLMs for screening
  • LLMs for medical knowledge retrieval
  • explanations of LLMs for healthcare applications

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Published Papers (1 paper)

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Research

13 pages, 470 KiB  
Article
Towards Early Maternal Morbidity Risk Identification by Concept Extraction from Clinical Notes in Spanish Using Fine-Tuned Transformer-Based Models
by Andrés F. Giraldo-Forero, Maria C. Durango, Santiago Rúa, Ever A. Torres-Silva, Sara Arango-Valencia, José F. Florez-Arango and Andrés Orozco-Duque
Appl. Syst. Innov. 2025, 8(3), 78; https://doi.org/10.3390/asi8030078 - 11 Jun 2025
Viewed by 777
Abstract
Early detection of morbidities that complicate pregnancy improves health outcomes in low- and middle-income countries. Automatic revision of electronic health records (EHRs) can help identify such morbidity risks. There is a lack of corpora to train models in Spanish in specific domains, and [...] Read more.
Early detection of morbidities that complicate pregnancy improves health outcomes in low- and middle-income countries. Automatic revision of electronic health records (EHRs) can help identify such morbidity risks. There is a lack of corpora to train models in Spanish in specific domains, and there are no models specialized in maternal EHRs. This study aims to develop a fine-tuned model that detects clinical concepts using a built database with text extracted from maternal EHRs in Spanish. We created a corpus with 13.998 annotations from 200 clinical notes in Spanish associated with EHRs obtained from a reference institution of high obstetric risk in Colombia. Using the Beginning–Inside–Outside tagging scheme, we fine-tuned five different transformer-based models to classify between 16 classes associated with eight entities. The best model achieved a macro F1 score of 0.55 ± 0.03. The entities with the best performance were signs, symptoms, and negations, with exact F1 scores of 0.714 and 0.726, respectively. The lower scores were associated with those classes with fewer observations. Even though our dataset is shorter in size and more diverse in entity types than other datasets in Spanish, our results are comparable to other state-of-the-art named entity recognition models fine-tuned in Spanish and the biomedical domain. This work introduces the first fine-tuning of a model for named entity recognition specifically designed for maternal EHRs. Our results can be used as a base to develop new models to extract concepts in the maternal–fetal domains and help healthcare providers detect morbidities that complicate pregnancy early. Full article
(This article belongs to the Special Issue Large Language Models: Theories, Methodologies and Real-World Applications in Healthcare)
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