ArticleNPJ digital medicine2025
Personalizing prostate cancer education for patients using an EHR-Integrated LLM agent.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prostate cancer research on social media platforms: a bibliometric and thematic analysis.Frontiers in oncology · 2026Pooled it
- Preliminary Evaluation of a Large Language Model-Powered Chatbot for Osteoporosis Self-Management Education: Formative Randomized Controlled Trial.JMIR formative research · 2026Trial
- The daily dose: Early usability of an LLM tool for patient summaries and trial matching in radiation oncology.Clinical and translational radiation oncology · 2026Article
- Development and Nationwide Multicentre Evaluation of Guideline-Grounded Large Language Model Chatbots to Support Patient Self-Management and Education in Rheumatology.Journal of medical systems · 2026Article
- Experiences of mHealth use in head and neck cancer care: a thematic synthesis guided by the Unified Theory of Acceptance and Use of Technology.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Review
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Tailored Personas of Online Health Information-Seeking Behaviors Among Men With Prostate Cancer Receiving Androgen Deprivation Therapy: Qualitative Study.Journal of medical Internet research · 2026Article
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Research through Evaluation for Large Language Model in Patient-Clinician Communications.Research square · 2026Article
- Retrieval-Augmented Generation in Oncology: Promises, Pitfalls, and Early Applications.AI in precision oncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Cancer patients often lack timely education and personalized support due to clinician workload. This quality improvement study develops and evaluates a Large Language Model (LLM) agent, MedEduChat, which is integrated with the clinic's electronic health records (EHR) and designed to enhance prostate cancer patient education. Fifteen non-metastatic prostate cancer patients and three clinicians recruited from the Mayo Clinic interacted with the agent between May 2024 and April 2025. Findings showed that MedEduChat has a high usability score (UMUX = 83.7/100) and improves patients' health confidence (Health Confidence Score rose from 9.9 to 13.9). Clinicians evaluated the patient-chat interaction history and rated MedEduChat as highly correct (2.9/3), complete (2.7/3), and safe (2.7/3), with moderate personalization (2.3/3). This study highlights the potential of LLM agents to improve patient engagement and health education.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.