ArticleFrontiers in digital health2025
Feasibility of a multimodal AI-based clinical assessment platform in emergency care: an exploratory pilot study.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Workflow Bottlenecks and Staff Readiness in an NHS Emergency Urology Clinic: A Prospective Service Evaluation to Inform Future AI-Supported Triage.Healthcare (Basel, Switzerland) · 2026Article
- The Power of Multimodality in Multimodal Large Language Models, Unimodal ChatGPT 5.0, and Human Clinical Experts on a Wound Care Certification Examination: Cross-Sectional Comparative Study.JMIR formative research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Overcrowding in emergency departments (EDs) is a key challenge in modern healthcare, affecting not only patient and staff comfort but also mortality rates and quality of care. Artificial intelligence (AI) offers the potential to optimize ED workflows by automating processes such as triage, history-taking and documentation. To explore a potential approach to overcrowding, we developed a multimodal and modular AI-based platform that integrates these functions into a single system. This exploratory pilot study investigated the feasibility of implementing the platform, focusing particularly on usability and patient trust in the system. Methods: Ambulatory patients triaged as non-urgent at the Marburg University Hospital ED were recruited. After providing written consent, they underwent an AI-supported initial assessment, including vital sign monitoring, automated triage, suspected diagnosis and automatic report generation. Participants then completed validated questionnaires on usability, Trust in Automation (TiA), and a supplementary self-developed survey. Results: A total of 20 patients were enrolled (70% female, 30% male; mean age 45.1 years), with an average interaction time of 10.6 min. The majority (80%) reported feeling safe, satisfied, and willing to recommend the system, while areas for improvement were identified regarding patient inclusion in decision-making and the perceived quality of information received. Usability was rated as excellent, with a mean System Usability Scale (SUS) score of 90.6. Although familiarity with the system was low, trust-related measures assessed using the TiA questionnaire were generally high. Conclusion: This exploratory pilot study demonstrates the feasibility and user acceptance of a multimodal AI platform in an ED setting. The system achieved high patient satisfaction, excellent usability, and a generally high level of trust. While these findings are limited to feasibility and perception, they indicate that such systems could serve as a basis for multicenter studies that directly evaluate impacts on triage accuracy, patient engagement, and clinical efficiency.
Indexed as
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.