ArticleThe American journal of medicine2026
Impact of patient communication style on agentic AI-generated clinical advice in E-medicine.
Article in The American journal of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative large language models in the clinical management of Alzheimer's disease and mild cognitive impairment.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Large Language Models in Clinical Neurology: A Systematic Review.Research square · 2026Article
- When Agentic LLMs Trust Poisoned Tools: Vulnerability of Clinical LLMs to Adversarial Guidelines.Research square · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundE-medicine use has surged, and health systems are exploring large language models (LLMs) for message triage. However, it is still unknown whether patient tone alone alters AI-generated clinical or administrative decisions.
methodsWe created 1000 clinician-validated primary-care vignettes (500 clinical, 500 sick-leave) and presented each in eight communication styles. Five agentic LLMs generated structured outputs for triage urgency, sick-leave decisions and other outputs. Differences from the neutral control were assessed using chi-square tests (Cramér's V) and t-tests (Cohen's d), with FDR correction. As external validation, 40 real patient e-messages from a large health network were processed using the same pipeline.
resultsAcross 120,000 agent runs, patient tone produced clear and reproducible shifts. Urgent, threatening, and demanding framings increased same-day or urgent care from 14% to 37%-63% (V up to 0.69, P < 0.001). Medication advice shifted modestly toward prescription options (Rx from 5% to 7%-9%, P < 0.001). Emotional tone increased empathy-based responses from 62% to 70%-86% (P < 0.001). In sick-leave tasks, threatening tone reduced approvals (58% to 50%) and granted days (2.60 to 2.36; d = -0.12), while emotional tone slightly increased both. The real-world validation showed the same directional effects. It confirmed that tone influenced model outputs even in authentic messages.
conclusionsAgentic LLMs treated patient tone as clinical input, altering triage, follow-up, prescribing, and sick-leave decisions despite identical symptoms. These tone-sensitive shifts may introduce hidden biases, affect resource use, and enable misuse in E-medicine workflows.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.