Large Language Models For Medical Applications
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Large Language Models for Medical Applications
Author: Ariel Soares Teles
language: en
Publisher: Frontiers Media SA
Release Date: 2025-06-17
Large Language Models (LLMs) have revolutionized various domains with their capabilities to understand, generate, and process human language at scale. In the realm of healthcare, LLMs hold immense potential to transform how medical information is analyzed, communicated, and utilized. This Research Topic delves into the applications, challenges, and future prospects of employing LLMs in medical settings. The adoption of LLMs in medical settings holds the promise of enhancing clinical workflows, improving patient outcomes, and facilitating more informed decision-making processes. These models, built upon vast corpora of medical literature, patient records, and clinical guidelines, possess the capacity to sift through and distil complex information, providing health professionals with timely insights and recommendations tailored to individual patient needs.
Artificial Intelligence-Empowered Bio-medical Applications
Author: Dimitrios P. Panagoulias
language: en
Publisher: Springer Nature
Release Date: 2025-07-19
The book delves into advancements in personalized medicine, highlighting the transition from generalized treatments to tailored strategies through AI and machine learning. It first emphasizes the role of biomarkers in training predictive models and neural networks, enhancing disease diagnosis and patient management. It then explores AI-driven healthcare systems, particularly the use of microservices to improve scalability and management. Additionally, it examines regulatory challenges, the need for AI explainability, and the PINXEL framework, which defines explainability requirements using the technology acceptance model (TAM) and the diffusion of innovation theory (DOI). Furthermore, the book evaluates the capabilities of large language models, including ChatGPT and GPT-4V, in medical applications, with a focus on diagnosis and structured assessments in general pathology. Lastly, it introduces an AI-powered system for primary care diagnosis that integrates language models, machine learning, and rule-based systems. The interactive AI assistants “Med|Primary AI assistant” and “Dermacen Analytica” leverage natural language processing, image analysis, and multi-modal AI to enhance patient interactions and provide healthcare professionals with high-accuracy, personalized diagnostic support. By taking a holistic approach, the book underscores the integration of AI into healthcare, aiming to support medical professionals in patient diagnosis and management with precision and adaptability.
Large Language Models for Automatic Deidentification of Electronic Health Record Notes
Author: Jitendra Jonnagaddala
language: en
Publisher: Springer Nature
Release Date: 2025-01-25
This volume constitutes the refereed proceedings of the International Workshop on Deidentification of Electronic Health Record Notes, IW-DMRN 2024, held on January 15, 2024, in Kaohsiung, Taiwan. The 15 full papers were carefully reviewed and selected from 30 submissions. The conference focuses on medical data analysis, enhancing medication safety, and optimizing medical care efficiency.