Reference no: EM133572041

Case Study:The digital health landscape is evolving at an unprecedented pace, and one area that requires particular attention is the querying and exploration of the details within electronic medical records (EMRs). Large Language Models (LLM), have amazing potential in this space however it is difficult to directly question the knowledge contained within an LLM. Benchmarks show LLMs have significant gaps in the comprehension of factual data and it's difficult to reason about concepts such as time. They are also prone to hallucinations (generation of false or misleading information) and vary in their results for the same queries. Inaccurate or incomplete querying of EMRs can have significant implications for patient care and health outcomes. This assignment aims to investigate how LLMs can be made to enhance the reliability, accuracy, and consistency of querying medical records; to enhance reasoning skills concerning abstract ideas and comprehension of factual data; and to reduce misinformation events such as hallucinations.

Questions:

• The current state of industry and technical challenges of using LLMs for EMR queries including their susceptibility against common types of errors or malfunctions

• Describe novel methods of improvement to LLM techniques for more reliable EMR queries

• Consider in greater detail how interactions between LLMs and knowledge graphs can particularly be used to improve reasoning and comprehension of the factual data contained within the EMR record

• Identify further barriers and future system integration challenges to using these methods described above for querying EMR systems within the healthcare domain