About This Special Issue
In recent years, large language models, such as GPT-3.5, Bard and its variants, have revolutionized the field of natural language processing (NLP). These advanced AI models have shown remarkable capabilities in understanding and generating human-like text, sparking considerable interest and application in various domains, including medicine and healthcare. As the intersection of AI and medicine continues to evolve, it is crucial to explore the potential and limitations of large language models in medical contexts.
This special issue aims to shed light on the role of large language models in medicine, medical research, their impact on patient care, and their potential in advancing the field of healthcare. We invite researchers, healthcare professionals, and AI experts to contribute their insights and expertise to enrich the understanding of this rapidly evolving domain.
Types of articles welcomed: Original research articles, review articles, case studies, letter to editors, correspondences, case series, research letters, systematic reviews and meta-analysis etc.
By bringing together cutting-edge research and practical insights from leading experts, this special issue seeks to establish a comprehensive and informed understanding of the use of large language models in medical contexts. It is our aspiration that this collection of articles will stimulate further exploration, foster interdisciplinary collaborations, and contribute to the responsible and effective integration of AI-driven language models in the advancement of medical research and healthcare delivery.
We look forward to receiving your contributions and collectively shaping the future of large language models in the medical domain.
Potential topics include, but are not limited to:
- The application of large language models in clinical decision support
- Ethical considerations and privacy implications of using language models in healthcare
- Natural language understanding and generation in medical chatbots and virtual assistants
- Large language models in drug discovery and medical literature analysis
- Addressing biases and improving fairness in medical language models
- Integrating language models into electronic health record systems for enhanced data processing
- Multimodal integration of language models with medical imaging and clinical data
- Evaluating the safety and reliability of large language models in medical settings
- Large language models for medical education and training
- Collaborative efforts between AI language models and medical experts for improved patient outcomes