WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patients

arXiv:2506.06104 · cs.HC, cs.CV · Submitted 2025-06-06 · Read on arXiv

cs.HC, cs.CV

Submitted: 2025-06-06

Updated: 2026-09-17

Journal ref: ACM Transactions on Computing for Healthcare - Special Issue on Human Centered Computing in Healthcare (2026)

DOI: 10.1145/3810243

License: http://creativecommons.org/licenses/by/4.0/

The gist: The rising prevalence of chronic wounds, especially in aging populations, presents a significant healthcare challenge due to prolonged hospitalizations, elevated costs, and reduced patient quality of

Terminology

Fidelity — 37× in this paper · explained in 2 episodes
Transformer — 22× in this paper · explained in 2 episodes
WUND APP — 12× in this paper
Video Consultation — 11× in this paper
Appointment Scheduling — 8× in this paper
Core — 8× in this paper · explained in 2 episodes
Conference on Computer Vision and Pattern Recognition — 7× in this paper
Perceived Usefulness — 7× in this paper
Accuracy — 6× in this paper · explained in 1 episode
Perceived Ease of Use — 6× in this paper
Robustness — 5× in this paper · explained in 5 episodes
Artificial Intelligence — 4× in this paper
Benchmark — 3× in this paper · explained in 2 episodes
Benchmarking — 3× in this paper · explained in 4 episodes
Calibration — 3× in this paper · explained in 7 episodes
Consistency — 3× in this paper · explained in 3 episodes
Segmentation Masks — 3× in this paper · explained in 2 episodes
Adaptation — 2× in this paper · explained in 1 episode
Alignment — 2× in this paper
CVF International Conference on Computer Vision — 2× in this paper
Cognitive Load — 2× in this paper · explained in 2 episodes
Distribution Shift — 2× in this paper · explained in 9 episodes
Elias — 2× in this paper · explained in 2 episodes
Generalizability — 2× in this paper · explained in 6 episodes
Generalization — 2× in this paper · explained in 13 episodes
Homogeneity — 2× in this paper · explained in 2 episodes
In International Conference on Learning Representations — 2× in this paper
Medical Image Computing and Computer-Assisted Intervention — 2× in this paper
Technology Acceptance Model — 2× in this paper
alpha — 2× in this paper
Abundance — 1× in this paper · explained in 1 episode
Adam — 1× in this paper · explained in 2 episodes
Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
Distillation — 1× in this paper · explained in 2 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Iterative Refinement — 1× in this paper · explained in 3 episodes
Trace — 1× in this paper · explained in 2 episodes
Transformer Architecture — 1× in this paper · explained in 3 episodes
Validation of the Affinity for Technology Interaction — 1× in this paper
Verification — 1× in this paper · explained in 2 episodes

Abstract

The rising prevalence of chronic wounds, especially in aging populations, presents a significant healthcare challenge due to prolonged hospitalizations, elevated costs, and reduced patient quality of life. Traditional wound care is resource-intensive, requiring frequent in-person visits that strain both patients and healthcare professionals (HCPs). Thus, we present WoundAIssist, a patient-centered, AI-driven mobile application supporting telemedical wound care. WoundAIssist enables patients to document wounds at home via photographs and questionnaires, while physicians remain engaged in the care process through remote monitoring and video consultations. A distinguishing feature is an integrated lightweight deep learning model for on-device wound segmentation, guiding users during image capture. Combined with patient-reported data and server-side AI analysis, this enables continuous monitoring of wound healing progression. Developed through an iterative, user-centered process involving patients and domain experts, WoundAIssist prioritizes an user-friendly design, particularly for elderly patients. A conclusive usability study with patients and dermatologists reported excellent usability, good app quality, and favorable perceptions of the AI-driven wound recognition. Our main contribution is two-fold: (I) the development and (II) evaluation of WoundAIssist, an easy-to-use yet comprehensive telehealth solution designed to bridge the gap between patients and HCPs. Additionally, we synthesize design insights for remote patient monitoring apps, derived from over three years of interdisciplinary research, that may inform the development of similar digital health tools across clinical domains.

WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patients

Terminology

Abstract

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