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

arXiv: https://arxiv.org/abs/2506.06104 · HTML: https://mlsanity.com/paper/2506.06104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [Semantic Segmentation using Vision Transformers: A survey](https://mlsanity.com/paper/2305.03273/) (2023)
- [Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2010.05855/) (2020)
- [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022)
- [FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation](https://mlsanity.com/paper/2305.02961/) (2023)
- [When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing](https://mlsanity.com/paper/2107.02947/) (2021)

## Built on by (most cited first, 1 of 1)

- [SurgWound-Bench: A Benchmark for Surgical Wound Diagnosis](https://mlsanity.com/paper/2508.15189/) (2025)

## Abstract (arXiv)

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.