Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
Large Language Models are being actively integrated with electronic health records to improve clinical diagnosis and decision-making, as evidenced by the development of frameworks like medIKAL [1] and AgentClinic [2]. Furthermore, researchers utilize LLMs to interpret free-form health records [3] and employ structured output techniques to ensure compatibility with electronic health record systems {fact:3, fact:5}.
Facts (5)
Sources
A framework to assess clinical safety and hallucination rates of LLMs ... nature.com 3 facts
claimUsing prompts with function calling in Large Language Models is useful for ensuring that generated outputs adhere to the specific structures required for different electronic health records.
procedureFunction calls and JSON-based output (Experiments 6, 9, 10, 11) instructed LLMs to generate responses in structured JSON format to optimize integration with primary care electronic health record systems.
referenceJia et al. (2025) introduced medIKAL, a framework that integrates knowledge graphs as assistants for large language models to enhance clinical diagnosis on electronic medical records.
Understanding LLM Understanding skywritingspress.ca 1 fact
procedureResearchers at McGill and MILA used deep learning to interpret clinician thinking by pre-training on hundreds of millions of general sentences and applying large language models to over 4,000 free-form health records to distinguish confirmed from suspected autism cases.
A Comprehensive Benchmark and Evaluation Framework for Multi ... arxiv.org 1 fact
referenceAgentClinic is a multimodal agent environment that treats clinical decision-making as a sequential task involving external tools and electronic health records, demonstrating that interactive diagnosis is more challenging for Large Language Models than static answering.