Impact of Patient Communication Style on Agentic AI-Generated Clinical Advice in E-Medicine
Agentic 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.
Authors
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Mahmud Omar (ORCID: https://orcid.org/0009-0001-0438-0827)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Alexander W. Charney (ORCID: https://orcid.org/0000-0001-8135-6858)
- Reem Agbareia (ORCID: https://orcid.org/0009-0000-8030-9232)
- Alon Gorenshtein (ORCID: https://orcid.org/0009-0000-7542-8608)
- Robert Freeman (ORCID: https://orcid.org/0000-0003-4413-9963)
- Kareem Hijazi
Institutions
- Mount Sinai Health System (US)
- Hadassah Medical Center (IL)
- Maccabi Healthcare Services (IL)
Publication Details
- Journal
- medRxiv
- Published
- 2025-12-04
- DOI
- https://doi.org/10.64898/2025.12.02.25341475
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health