HerHealthGPT is an evidence‑grounded conversational AI designed for women's reproductive health. It uses retrieval‑augmented generation, context‑aware clarification, and source validation to avoid premature conclusions and to ask follow‑up questions when user queries are incomplete. Evaluation on 540 messages and a user study with 30 adults showed improved accuracy and a high preference for the retrieval‑enabled version.
By Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi, Hana Essam Sayed Ahmed Amrya, Mariam Mousa
arXiv:2605. 18937v2 Announce Type: replace Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights.
By Rory Sayres, Kejia Chen, Ayush Jain, Matthew Thompson, Jonathan Richina, Xiang Yin, Jimmy Hu, Fan Zhang, Bob Lou, Mike Sanchez, Ines Mezerreg, Meredith Schreier, Hamsa Subramaniam, I-Ching Lee, Yugang Jia, Daniel Mcduff, Yossi Matias, Avinatan Hassidim, Dale Webster, Yun Liu, Jackie Barr, Quang Duong
arXiv:2608. 19875v1 Announce Type: cross Abstract: Patients often submit short, underspecified queries to healthcare chatbots that lack the patient-specific information needed to determine an appropriate response.
By Mahyar Abbasian, Saba A. Farahani, Arshia Ilaty, Hung Cao, Ramesh Jain, Amir M. Rahmani
arXiv:2609.07601v1 Announce Type: cross
Abstract: The application of large language models (LLMs) to personalized medical assistants has garnered growing interest. However, existing medical benchmark...
By Jun Xiang, Zhijie Bao, Rong Hu, Kaizhou Qin, Wei Chen, Zhongyu Wei
arXiv:2602.01995v2 Announce Type: replace
Abstract: Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomp...
By Jeongmoon Won, Seungwon Kook, Yohan Jo
ClinAgent is a conversational system that uses a ReAct-based LLM agent to retrieve and synthesize clinical trial information from multiple sources such as ClinicalTrials.gov, PubMed, and a local dataset. The agent iteratively reasons over user queries, selects appropriate tools, and refines its actions to provide grounded, up-to-date responses in natural language across multi-turn interactions. Evaluation across three phases shows that DeepSeek (thinking mode) excels in planning quality while Gemini 3.0 Flash delivers the highest overall performance and expert ratings, demonstrating the promise of agentic AI for improving clinical trial data access.
By Antonino Vaccarella, Riccardo Cantini, Domenico Talia, Paolo Trunfio, Marianna Talia, Rosamaria Lappano, Marcello Maggiolini
PIA is a personal intelligence agent that works alongside a consumer health agent to convert health conversations into structured clinical records and to transform those records into a synthesized understanding of the user. It uses a memory system with four controls—extraction, memory, retrieval, and understanding—each supported by a health module that includes a schema, medical alias dictionary, knowledge graph, and temporal rules. The agent demonstrates that deeper memory injection—from simple recall to a health snapshot to a causal trajectory—yields progressively richer answers, while also revealing challenges such as missing self‑reported data, the influence of question phrasing, and the presence of structural noise in causal links.
By Jeonghun Yoon, Dongchan Kim, Hongyeon Yu, Young-Bum Kim, Jaegul Choo
arXiv:2601.09717v2 Announce Type: replace-cross
Abstract: Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but al...
By Yiwei Yan, Guanfeng Liu
arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.
By Aueaphum Aueawatthanaphisut
The paper introduces BRIE, a scalable framework that automatically creates question–answer pairs from longitudinal electronic health record notes, validated by nineteen clinicians. It offers a continuously maintainable benchmark for evaluating large language models in clinical settings, addressing limitations of manual, costly, and quickly outdated existing benchmarks. Experiments across nine LLMs and five inference strategies reveal that even state‑of‑the‑art systems often miss clinically important information, especially for synthesis‑heavy queries.
arXiv:2607.13038v2 Announce Type: replace-cross
Abstract: Conversational AI can improve access to public health information, but public-facing healthcare applications require safeguards against inapp...
By Ben Torkian, Jun Zhou
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
By Sandhanakrishnan Ravichandran, Shivesh Kumar, Rogerio Corga Da Silva, Miguel Romano, Reinhard Berkels, Michiel van der Heijden, Olivier Fail, Valentine Emmanuel Gnanapragasam