title: Medical large language models are easily distracted
summary: Large language models (LLMs) have the potential to transform medicine, but
  real-world clinical scenarios contain extraneous information that can hinder
  performance. The rise of assistive technologies like ambient dictation, which
  automatically generates draft notes from live patient encounters, has the
  potential to introduce additional noise making it crucial to assess the ability
  of LLM's to filter relevant data. To investigate this, we developed
  MedDistractQA, a benchmark using USMLE-style questions embedded with simulated
  real-world distractions. Our findings show that distracting statements
  (polysemous words with clinical meanings used in a non-clinical context or
  references to unrelated health conditions) can reduce LLM accuracy by up to
  17.9%. Commonly proposed solutions to improve model performance such as
  retrieval-augmented generation (RAG) and medical fine-tuning did not change
  this effect and in some cases introduced their own confounders and further
  degraded performance. Our findings suggest that LLMs natively lack the logical
  mechanisms necessary to distinguish relevant from irrelevant clinical
  information, posing challenges for real-world applications. MedDistractQA and
  our results highlights the need for robust mitigation strategies to enhance LLM
  resilience to extraneous information.
paper_authors._id: 67ee95f48b0b5419e49c37dc
paper_authors.name: Krithik Vishwanath
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-03T14:06:45.697Z
paper_authors._id: 66a02bb71bb371390c4ae519
paper_authors.avatarUrl: /avatars/2c4791516aae0b20a9c9ce0542dc966e.svg
paper_authors.fullname: Krithik Vishwanath
paper_authors.type: user
paper_authors.user: KrithikV
paper_authors._id: 67ee95f48b0b5419e49c37dd
paper_authors.name: Anton Alyakin
paper_authors._id: 67ee95f48b0b5419e49c37de
paper_authors.name: Daniel Alexander Alber
paper_authors._id: 67ee95f48b0b5419e49c37df
paper_authors.name: Jin Vivian Lee
paper_authors._id: 67ee95f48b0b5419e49c37e0
paper_authors.name: Douglas Kondziolka
paper_authors._id: 67ee95f48b0b5419e49c37e1
paper_authors.name: Eric Karl Oermann
paper_title: Medical large language models are easily distracted
paper_summary: Large language models (LLMs) have the potential to transform medicine, but
  real-world clinical scenarios contain extraneous information that can hinder
  performance. The rise of assistive technologies like ambient dictation, which
  automatically generates draft notes from live patient encounters, has the
  potential to introduce additional noise making it crucial to assess the ability
  of LLM's to filter relevant data. To investigate this, we developed
  MedDistractQA, a benchmark using USMLE-style questions embedded with simulated
  real-world distractions. Our findings show that distracting statements
  (polysemous words with clinical meanings used in a non-clinical context or
  references to unrelated health conditions) can reduce LLM accuracy by up to
  17.9%. Commonly proposed solutions to improve model performance such as
  retrieval-augmented generation (RAG) and medical fine-tuning did not change
  this effect and in some cases introduced their own confounders and further
  degraded performance. Our findings suggest that LLMs natively lack the logical
  mechanisms necessary to distinguish relevant from irrelevant clinical
  information, posing challenges for real-world applications. MedDistractQA and
  our results highlights the need for robust mitigation strategies to enhance LLM
  resilience to extraneous information.
paper_ai_summary: MedDistractQA benchmark reveals LLMs struggle with filtering relevant clinical information amidst distractions, challenging their real-world applicability.
paper_ai_keywords: LLMs assistive technologies ambient dictation MedDistractQA USMLE-style questions distracting statements polysemous words clinical meanings retrieval-augmented generation medical fine-tuning logical mechanisms robust mitigation strategies