Large Agent Models in Patient Care and Clinical Communication Infrastructure: Applications and Challenges
Matthias Rüb, Jan Herbst, Oliver Mey, Ralf Irmer, Christoph Lipps, Hans Dieter Schotten · 2024
The ongoing developments in the field of Artificial Intelligence (AI) methods, Large Language Models (LLMs), and Large Agent Models (LAMs) are raising awareness and open up a wide range of new application scenarios. One of these is the healthcare sector. Therefore, this work develops a vision on the application of LLMs/LAMs and emphasizes their potential to improve the healthcare system, support the administration of patient data, and assist clinical staff with multimodal support systems and diagnosis and therapy recommendations. Additionally, this work addresses the role of LLMs/LAMs in terms of the transformation of telecommunication within healthcare, especially through automation. Thereby, the work not only addresses the potential applications but also describes the risks associated with their use. Furthermore, the work addresses the specific dangers arising when LLMs become more responsible in an automated healthcare wireless communication framework in the form of LAMs. By examining both the risks and benefits of implementing LLMs in healthcare telecoms orchestration, the work provides an overview of the potential applications and challenges of LLMs in this area.