HTA Considerations for Large Language Models in Healthcare
Speaker(s)
Leonard C1, Unsworth H2, Warttig S3, Gildea L1, Mordin M4, Ling C3
1RTI Health Solutions, Manchester, LAN, UK, 2Digital Cancer Research, Manchester, UK, 3RTI Health Solutions, Manchester, UK, 4RTI Health Solutions, Ann Arbor, MI, USA
Presentation Documents
LLMs are generative AI models, trained on extremely large data sets to be effective in various natural language processing tasks. The latest generation of LLMs have sufficient language-handling ability to perform healthcare-related tasks, including text analysis and summarisation, diagnostic assistance, answering medical queries, and image captioning.
LLMs have been identified as having the potential to improve healthcare through improved data handling, process automation, service quality, personalised care, and faster diagnosis. Med-PaLM, Bloom, and LLaMA 3 are current examples of LLMs in healthcare. Med-PaLM provides a range of functions such as diagnostic assistance, synthesising and communicating information from images and other medical data, and discussing results with clinicians through natural language dialogue.
We consider the complexity and requirements for evaluating LLMs and suggest updates to the ESF to help HTA bodies and developers of DHTs meet standards to successfully approach HTA for LLMs in healthcare.
Code
HTA10
Topic
Methodological & Statistical Research
Topic Subcategory
Artificial Intelligence, Machine Learning, Predictive Analytics
Disease
No Additional Disease & Conditions/Specialized Treatment Areas