Large Language Models for Medicine: Systematic Review Categorizes Training and Evaluation Approaches

The rapid development of artificial intelligence presents exciting opportunities for healthcare, and large language models now stand at the forefront of this progress. Zhiyu Kan, Wensheng Gan, and Zhenlian Qi from Jinan University, alongside Philip S. Yu, comprehensively investigate the current state of these models in medicine, offering a crucial overview of a rapidly evolving field. Their work systematically categorises existing medical language models based on how they learn, and also clarifies how best to assess their performance, revealing both the considerable potential and the existing limitations of this technology. By providing a clear framework for understanding and evaluating medical language models, this research establishes a vital foundation for future innovation and ultimately aims to accelerate the development of more effective, AI-driven healthcare solutions.

Large Language Models in Medical Research

This comprehensive analysis reveals a rapidly expanding research area focused on Large Language Models (LLMs) and their applications within the medical field. The sheer volume of published research demonstrates intense interest in leveraging these models for a wide range of healthcare applications, from fundamental capabilities to specific clinical uses, ethical considerations, and technical challenges. The field is quickly progressing from initial exploration to focused research and development. Research encompasses foundational studies of LLMs, their architecture, training methods, and general capabilities.

A major theme involves applying LLMs to assist doctors in making diagnoses, suggesting treatments, and providing personalized care. A growing area focuses on multimodal LLMs, which can process both text and images, such as medical scans and pathology slides. Critical research also addresses evaluating LLM performance, identifying biases, and measuring accuracy. Further research explores the ethical challenges of using LLMs in healthcare, including patient privacy, data security, and the potential for misinformation. Technical challenges, such as data scarcity and the need for specialized training, are also being actively addressed.

Studies also focus on applying LLMs within specific medical domains, like radiology and genomics, and integrating them with wearable sensors for continuous health monitoring. The exploration of combining LLMs with technologies like blockchain and the metaverse suggests a vision for a more connected and intelligent healthcare ecosystem. Key trends reveal a major focus on multimodality, with increasing research on models that combine text and images, essential for medical diagnosis. Emphasis on evaluation and safety highlights the importance of ensuring these models are reliable and accurate for healthcare use. Ethical concerns are also prominent, demonstrating a growing awareness of the potential risks and challenges.

Medical LLMs, Training and Evaluation Approaches

This study presents a systematic review of large language models (LLMs) within the medical field, establishing a comprehensive understanding of their current capabilities and future potential. Researchers detail the evolution of LLMs, tracing their development from early N-gram models to sophisticated, transformer-based architectures like BERT and GPT-4, highlighting key breakthroughs in natural language processing. They meticulously analyze training techniques employed for medical models, alongside their adaptation to diverse healthcare settings and applications. The work categorizes medical LLMs based on variations in their training methodologies, and classifies evaluation approaches into two primary categories, providing a structured framework for assessing performance.

Researchers detail how these models are trained using massive datasets, enabling them to learn the intricate structure and patterns within language. They differentiate between generative LLMs, designed to create new text, and discriminative LLMs, which focus on categorizing or identifying patterns within existing data. This detailed comparison outlines the strengths and limitations of each approach, with generative models excelling in text creation and discriminative models better suited for classification and regression. Within the medical field, most LLMs are generative, allowing for interchangeable use of the terms “LLMs” and “generative LLMs” throughout the analysis. This rigorous categorization and comparative analysis establish a solid foundation for future research and development in medical LLMs.

Large Language Models Advance Medical Applications

Recent advances in artificial intelligence have yielded large language models (LLMs), demonstrating significant potential, particularly within the medical field. Researchers systematically reviewed the current state of LLMs in medicine, categorizing these models into generative and discriminative types, with generative models being most prevalent in medical applications. Generative models learn the underlying structure of language to create new content, while discriminative models focus on classifying or identifying patterns within data. The study reveals a rapidly growing body of research, with the number of published papers on LLMs in medicine increasing year on year, signifying broad application prospects and a rising interest in the field.

LLMs achieve their capabilities through deep learning and natural language processing, extensively trained on massive datasets to understand and generate human language. Key to their performance is the Transformer model, employing a self-attention framework that effectively addresses challenges in processing long text sequences. Within healthcare, LLMs offer transformative potential across clinical practice, medical education, and research. These models provide a new dimension for medical data evaluation, revealing subtle changes and trends previously difficult to capture, supporting early disease detection and intervention.

Furthermore, LLMs demonstrate the capacity to generate diagnostic opinions that, in some instances, surpass human experience, significantly improving diagnostic accuracy and efficiency. In treatment planning, LLMs facilitate personalized medicine by tailoring treatments to individual patient characteristics, disease progression, and treatment response. Currently, research is actively exploring LLM applications in diverse medical specialties, including dentistry, radiology, and general clinical practice. The models provide valuable support to doctors by retrieving relevant medical literature, case analyses, and expert recommendations. Through iterative training and data-driven insights, LLMs possess strong diagnostic judgment and respond quickly to doctors’ needs in diagnosing rare cases and formulating treatment strategies.

Medical LLMs, Advances and Open Challenges

This research presents a systematic review of recent advances in large language models (LLMs) specifically within the medical field. Researchers detail the evolution of LLMs, tracing their development from early N-gram models to sophisticated, transformer-based architectures like BERT and GPT-4, highlighting key breakthroughs in natural language processing. They categorize medical LLMs based on their training methodologies and classify evaluation approaches into two distinct categories, offering a comprehensive overview of the current landscape. The review identifies several current challenges facing medical LLMs, including issues with memory, data management, and the need for unified evaluation metrics.

Authors acknowledge the importance of addressing ethical considerations and ensuring patient safety as these models become more integrated into healthcare practices. Future research directions focus on refining training techniques, improving model evaluation, and establishing robust ethical guidelines. This work provides a valuable resource for understanding the current state of medical LLMs and charting a course for future innovation in this rapidly evolving field.