Archiv Detail

Vortragende Person/Vortragende Personen:
Diverse

In recent years, the rapid advancements in Large Language Models (LLMs), like GPT-4, Claude 3, Gemini, and others, have demonstrated their immense capabilities in understanding and generating natural language and showcased their immense potential across various domains. In the Medical Informatics (MI) community, there is significant interest in leveraging LLMs in a variety of tasks ranging from general tasks such as improving patient assessment, therapeutics, diagnostics and treatment, specialized tasks such as primary care, surgery and oncology to clinical support tasks such as medical assistants and clinical research tasks relating to trials, compliance and data management.

This workshop focuses on the diverse applications of advanced LLMs in MI, addressing the unique challenges, methodologies, and impacts on healthcare practices. We welcome the submission of original papers on all topics related to LLMs in medical informatics, with particular interest in but not limited to:

• Clinical Decision Support Systems: Utilizing LLMs to aid diagnosis, treatment planning, and personalized medicine.
• Information Extraction from Medical Reports: Workflows and the use of LLMS to extract relevant information for diagnosis and treatment of diseases.
• Medical Literature Analysis: Applying LLMs for summarizing, querying, and interpreting vast amounts of medical texts and research papers.
• Predictive Analytics: Leveraging LLMs for forecasting disease outbreaks, patient outcomes, and resource allocation in healthcare settings.
• Healthcare Education, Medical Training and Simulation: Using LLMs to create realistic training scenarios for medical education and procedural training.
• Ethical and Legal Implications of LLMs in Healthcare: Addressing concerns related to bias, privacy, and regulatory compliance.
• Medical data lifecycle: Using LLMs to enhance many aspects of the data lifecycle including the collection and analysis of clinical trials data and improving data quality in medical databases and registries.

Further information and registration