arXiv:2609.24814v1 Announce Type: cross Abstract: Advances in processing power, camera technologies, and mobile image analysis have made smartphones and other mobile devices, such as laptops, increas...
The paper introduces the Semantic Tri-view Pipeline, an interpretable system that automatically screens teledermatology photographs for gradability by analyzing epidermal micro-relief across up to three smartphone views. It uses a lightweight DeepLabV3+ model to segment micro-relief fidelity and aggregates the resulting spatial masks with logistic regression, leveraging viewpoint redundancy to improve robustness. Evaluated on the SCIN dataset, the approach raises the AUC from 0.81 to 0.96 on optically clear cases, offering real‑time, privacy‑by‑design feedback to filter ungradable photo sets before clinician review.
The paper presents TLNM, a Mask R‑CNN based system that detects, numbers, and segments teeth in smartphone photographs. It incorporates a masked gray‑world white‑balancing step and an anatomically constrained detection layer to handle patient‑generated variability. Evaluated on internal and external datasets, the model achieved high AP50, PQ, and F1 scores, demonstrating robust performance across diverse populations and imaging conditions.
WoundAIssist is an AI‑driven mobile application designed to support remote chronic wound care for elderly patients. It allows patients to capture wound images and complete questionnaires at home, while physicians monitor progress through remote video consultations and on‑device deep‑learning segmentation. A usability study involving patients and dermatologists found the app to be highly usable, of good quality, and positively received for its AI‑driven wound recognition.
arXiv:2607. 06598v1 Announce Type: cross Abstract: Heart rate measurement is one of the key requirements for real-time health monitoring, in particular for health caring of elderly people.
arXiv:2608. 12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarce, compute is limited, and clinical decision-making requires integrating heterogeneous modalities.
Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarce, compute is limited, and clinical decision-making requires integrating heterogeneous modalities. We introduce a cloud--edge collaborative architecture that addresses these constraints: lightweight, domain-specific models on the edge transform raw medical data into compact structured outputs, while a cloud LLM synthesizes these outputs into clinical summaries.
arXiv:2606. 19372v1 Announce Type: cross Abstract: We present Full-Self Diagnostics (FSD), a unified mathematical framework for recovering latent physiological states from unconstrained 9-second facial videos captured by consumer smartphones.
arXiv:2607. 07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease.
arXiv:2509. 07260v5 Announce Type: replace-cross Abstract: Mobile and wearable healthcare monitoring play a vital role in facilitating timely interventions, managing chronic health conditions, and ultimately improving individuals' quality of life.
arXiv:2608.21583v1 Announce Type: new Abstract: Oral cancer is a leading cause of mortality in low-to-middle-income countries, where a shortage of specialists delays diagnosis. While point-of-care sc...
We present VetClaw, an edge-cloud multimodal agentic system for early veterinary disease screening. VetClaw uses a camera module as an edge sensing device and sends captured images, together with optional symptom descriptions, to a server-hosted vision-language model for zero-shot disease classification.