A dual clinical-computational taxonomy for medical LLM reasoning plus a five-level 5k-sample benchmark showing specialists excel at diagnosis and general models at decision support/dialogue.
Medhalu: Hallucinations in responses to healthcare queries by large language models
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
LLMs induce pharmacological meaning primarily from affix cues in drug names, as revealed by a framework applied to 653 drugs and localized via activation patching to early-mid layers.
Evaluation of 6233 MedGPTs finds 25-30% with low factual accuracy, 33.6-54.3% violating operational thresholds, and 57% of action-enabled models lacking privacy disclosures.
Med-HEAL builds a hallucination dataset from BioMistral answers on EHRNoteQA via GPT-4o and human review, then shows self-critique improves accuracy in three of five tested LLMs without retraining.
MIRAGE combines a medical CLIP model, a diffusion generator, and an LLM into an accessible interface for retrieving and creating educational medical images and texts.
citing papers explorer
-
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
A dual clinical-computational taxonomy for medical LLM reasoning plus a five-level 5k-sample benchmark showing specialists excel at diagnosis and general models at decision support/dialogue.
-
What's in a Name? Morphological Shortcuts by LLMs in Pharmacology
LLMs induce pharmacological meaning primarily from affix cues in drug names, as revealed by a framework applied to 653 drugs and localized via activation patching to early-mid layers.
-
Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models
Evaluation of 6233 MedGPTs finds 25-30% with low factual accuracy, 33.6-54.3% violating operational thresholds, and 57% of action-enabled models lacking privacy disclosures.
-
Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
Med-HEAL builds a hallucination dataset from BioMistral answers on EHRNoteQA via GPT-4o and human review, then shows self-critique improves accuracy in three of five tested LLMs without retraining.
-
MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education
MIRAGE combines a medical CLIP model, a diffusion generator, and an LLM into an accessible interface for retrieving and creating educational medical images and texts.