aceLarge Language Models in Chronic Obstructive Pulmonary Disease Care and Health Management: A Scoping Review
Background: Chronic obstructive pulmonary disease (COPD) requires sustained patient education, self-management, medication use, pulmonary rehabilitation, symptom monitoring, and guideline-concordant clinical management. Large language models (LLMs) may expand access to conversational health information, but their value depends on more than factual accuracy. Objective: To map empirical evidence on LLMs and LLM-based generative artificial intelligence systems in COPD-related care and health management, summarize a…
LLMs often produced clinically acceptable responses in COPD patient education and information tasks, suggesting potential as supplementary, human-supervised tools.
The evidence base is small (eight studies) and dominated by model-response evaluation, with only two studies involving people with COPD, so real-world effects remain unknown.
Evidence
- Peer-reviewedJournal of Public Health and Preventive Medicine2026-10-19
How should this claim be treated?
Truvace Impact Record TRV-2026-1887, v1: “Large Language Models in Chronic Obstructive Pulmonary Disease Care and Health Management: A Scoping Review.” Truvace, 2026-10-19. /record/TRV-2026-1887 (accessed at citation time). sha256 bbc38dc2c8510a15…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1887 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.