arXiv:2607. 08625v1 Announce Type: new Abstract: Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment.
By Jo\~ao Matos, Olivia Buege, Donny Cheung, Gary S. Collins, Paula Dhiman, Nan Li, Bingyu Mao, Benjamin W. Nelson, Michail Ouroutzoglou, Paul Varghese, Jonathan Amar
arXiv:2607. 07824v1 Announce Type: cross Abstract: Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling.
By Jingyao Cai, Shuaijun Liu, Abdul Rehman, Yutong Guo, Qin Tian, Thomas Dolby, Sue Green, Chantel Cox, Xiaosong Yang
arXiv:2607.23648v2 Announce Type: replace
Abstract: Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The constru...
By Kaitong Weng, Lixin Liu, Zihao Liu, Bo Wang, Shiguang Ni
EmoMed is a multimodal medical consultation agent that tailors its responses to users' emotional states—such as anxiety, confusion, or urgency—while preserving clinical accuracy. It processes text and medical images, detects affect indicators, and adjusts tone, structure, and detail accordingly. The system ensures factual reliability through a dual retrieval mechanism that combines web-based fact‑checking with an API‑connected, continuously updated medical knowledge base, and it has been evaluated across seven state‑of‑the‑art language models using comprehensive metrics, showing that emotionally adaptive responses outperform neutral baselines without sacrificing accuracy.
By Ivan Nasonov, Nikita Glazkov, Ivan Makovetskiy, Mikhail Mozikov, Daniil Sukhorukov, Andrey Savchenko, Ilya Makarov
arXiv:2606. 17441v1 Announce Type: cross Abstract: Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies.
By Moritz Schlager, Friederike Jungmann, Samuel Schmidgall, Philipp Raffler, Franziska Hartl, Eva Wende, Paula Ro{\ss}m\"uller, Conrad Ketzer, Avinatan Hassidim, Dale R. Webster, Yossi Matias, Yun Liu, Daniel Rueckert, Mike Schaekermann, Paul Hager
The paper introduces SIC-Agents, a self‑improving framework designed to enhance simulation for pediatric serious illness communication (SIC) training. It presents two new benchmark suites—PitfallBench and DialogueBench—that assess simulators at both turn‑level and full‑dialogue levels, specifically addressing the unique challenges of multi‑party interactions and parental distress. Experiments demonstrate that SIC‑Agents surpasses static expert prompting, and the authors release the benchmarks for broader research use.
By Zihan Wang, Anita Marie Slominska, Rennie Bimman, Elizabeth Di Flumeri, Amanda Mayappo-Neeposh, Conall Francoeur, Tamara Ellen Carver, Xiao-Wen Chang, Doina Precup, Esin Darici Haritaoglu, Ismail Haritaoglu, Akshatha Arodi, Naomi Goloff