arXiv Computation and Language

PIA: A Personal Intelligence Agent Turning Health Conversations into Records and Records into Understanding

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.

arXiv AI

Sep 15

ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information

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

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arXiv AI

Jun 6

Evaluating the Utility of Personal Health Records in Personalized Health AI

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

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arXiv AI

Aug 25

GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations

arXiv:2510.03536v3 Announce Type: replace-cross Abstract: Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across mult...

By Zhaohan Meng, Zaiqiao Meng, Siwei Liu, Hao Xu, Ke Yuan, Iadh Ounis

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arXiv Computation and Language

Sep 23

From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health

The article surveys how large language models (LLMs) are being applied to mental health, outlining a three‑phase evolution: Phase I uses LLMs as passive information tools and pattern recognizers for assessment; Phase II employs them as empathetic conversationalists for stateless, in‑the‑moment interactions; Phase III aims to create longitudinal, personalized companions that act as stateful cognitive agents. It systematically reviews core technologies, agent architectures (Profile, Memory, Reasoning, Planning), and the datasets and benchmarks that support this progression, offering a coherent narrative and roadmap for future research. The survey also provides a curated resource list at https://github.com/Emo-gml/Awesome-Mental-Health-LLMs.

By He Hu, Yucheng Zhou, Qianning Wang, Yingjian Zou, Chiyuan Ma, Juzheng Si, Jianzhuang Liu, Zitong Yu, Laizhong Cui, Fei Ma, Qi Tian

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arXiv AI

Jul 16

A Self-Evolving Agent for Longitudinal Personal Health Management

arXiv:2607. 13940v1 Announce Type: new Abstract: Personal health management unfolds over repeated encounters, yet most health AI systems treat each request in isolation.

By Haoran Li, Jiebi Deng, Tong Jin, Jinghong Han, Yuxin Wang, Zexin Wang, Qingyi Si, Weikang Gong, Xiahai Zhuang, Jia You, Wei Cheng, Jianfeng Feng, Hongcheng Guo

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arXiv Computer Vision

Aug 25

MSM-Mem: A Universal Medical Structured Multimodal Memory Framework for Medical AI Agents

arXiv:2608.21810v1 Announce Type: cross Abstract: Clinical decision-making is inherently experience-driven: physicians progressively refine their reasoning by synthesizing patient history, multimodal...

By Md Asaduzzaman Jabin, Khoa Le, Lin Zhao, Tianming Liu

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arXiv AI

Jun 19

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

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

llms rag agents nlp multimodal

arXiv Computation and Language

Sep 22

Knowledge Graph-Augmented Ambient AI for Clinical Note Generation

arXiv:2609.22239v1 Announce Type: new Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...

By Jakir Hossain, Yi-Fei Zhao, Hongjian Wang, Minmei Shih, Katie Leigh Mullen, Ahmad P. Tafti, Leming Zhou, Manoj Purohit, William Hogan, Jay Zeng, Elizabeth Skidmore, Yanshan Wang

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arXiv AI

3d ago

Personalized State-Transition-Aware Memory for Clinical Agents

The paper introduces STAM, a state‑transition‑aware memory framework for large language model agents that process clinical records. STAM records changes in a patient’s state as new entries arrive, using semantic retrieval and typed clinical relations to separate current information (Active) from superseded or resolved information (History). During retrieval, a query‑dependent gate selects the appropriate historical memory, enabling accurate question answering and state‑maintenance diagnostics across four longitudinal clinical benchmarks.

By Maryam Haghifam, Zahra Rajabi, Yizhou Sun, Carlos Morato

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arXiv AI

Aug 21

A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries

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

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Hugging Face Trending Papers

Aug 2

TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents

Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.

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arXiv AI

Sep 1

MA-RAG: Multi-Agent Retrieval-Augmented Generation for Query-Driven Summarization of Longitudinal Parkinson's Disease Assessments

arXiv:2608.28624v1 Announce Type: cross Abstract: Accurate interpretation of single-visit and longitudinal clinical assessments for Parkinson's disease is time-consuming and often depends on speciali...

By Sana Alamgeera, Denise Goberta, Muhammad Irshad, Anne H. H. Ngu

llms rag agents nlp safety

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