arXiv AI

Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era

arXiv:2408. 02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era.

arXiv Machine Learning

Jun 4

Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis

arXiv:2512. 03296v2 Announce Type: replace-cross Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams.

By Hsiao-Ying Lu, Kwan-Liu Ma

arXiv AI

Jul 20

Cura 1T: Specialized Model for Agentic Healthcare

arXiv:2607. 15314v1 Announce Type: new Abstract: Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited.

By actAVA AI, :, Haolin Chen, Leon Qi, Steve Brown, Deon Metelski, Tao Xia, Joonyul Lee, Qixuan Wang, Kevin Riley, Frank Wang, Weiran Yao

llms agents benchmarks

arXiv AI

Jun 17

Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation

arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.

By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang

reinforcement-learning safety

arXiv AI

Jun 2

OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence

arXiv:2603. 14771v3 Announce Type: replace Abstract: Large Language Model (LLM)-based Collective Intelligence (CI) presents a promising approach to overcoming the data wall and continuously boosting the capabilities of LLM agents.

By Peigen Liu, Rui Ding, Yuren Mao, Ziyan Jiang, Yuxiang Ye, Yunjun Gao, Ying Zhang, Renjie Sun, Longbin Lai, Zhengping Qian

llms agents benchmarks

arXiv Machine Learning

Jun 30

Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture

arXiv:2606. 28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts.

By Md Mozaharul Mottalib, Rahmatollah Beheshti

arXiv AI

Jun 3

ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).

By Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li

llms multimodal benchmarks safety

arXiv AI

Jun 16

Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies

arXiv:2606. 16721v1 Announce Type: new Abstract: Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes.

By Ke Liu, Mengxuan Li, Yanyi Bao, Tianyun Zhang, Chong Chu, Jiajun Bu, Haishuai Wang

reinforcement-learning

arXiv AI

Jun 26

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

arXiv:2606. 26879v1 Announce Type: new Abstract: Synthetic data is increasingly used to enable the development and evaluation of AI systems in domains where access to real-world data is restricted.

By William Poulett

llms safety

arXiv Machine Learning

Aug 5

PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks

arXiv:2608. 02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources.

By Julia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan

multimodal benchmarks

Hugging Face Trending Papers

Jun 25

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

Synthetic data is increasingly used to enable the development and evaluation of AI systems in domains where access to real-world data is restricted. In healthcare, clinical documentation presents particular challenges due to its sensitivity.

llms safety

arXiv Machine Learning

Aug 3

What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches

arXiv:2607. 29090v1 Announce Type: new Abstract: Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identification of high-risk patients and targeted perioperative care.

By Yizhi Dong, Yuhe Ke, Hairil Rizal Abdullah, Yucheng Xing, Kevan Kai Bing Teo, Ling Huang, Mengling Feng

multimodal benchmarks

arXiv AI

Jun 11

Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark

arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.

By Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha

agents multimodal benchmarks safety

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