What are the challenges in implementing large language models in medicine?

Implementing large language models in medicine can present various challenges. One of the major hurdles is the collection of large-scale, high-quality medical data. Privacy concerns, data ownership issues, and the need for secure data storage make it difficult to gather a comprehensive dataset for training these models. Additionally, the specific domain knowledge required to understand medical terminologies and concepts requires careful curation and annotation. Another challenge lies in the interpretability and explainability of these models. In medicine, it is crucial to have a clear understanding of the reasoning behind model predictions for trust and accountability. Finally, the deployment of large language models in resource-constrained environments, such as hospitals or clinics with limited computational resources, can be a significant hurdle. Addressing these challenges is critical to ensure the successful implementation of large language models in the field of medicine.

This mind map was published on 25 July 2023 and has been viewed 107 times.