Clinical image recording is often poorly standardized. The use of personal devices and messaging platforms results in data fragmentation and limited accessibility. Previous mobile solutions prioritized secure image capture and electronic health record integration, yet offered minimal support for structured organization and efficient retrieval. This report describes the design and development of CliniCAM, a mobile health application for structured clinical image capture, annotation, tagging, retrieval, and dataset generation. CliniCAM was developed using FlutterFlow (FlutterFlow Inc., Mountain View, CA, USA) with a Firebase backend (Google, Mountain View, CA, USA). Firestore manages structured data, and Firebase Storage handles image management. The application supports in-app image capture, patient association, free-text annotation, and assignment of custom tags. Users can search by patient, free-text, or tag. Data export is enabled through Google Sign-In and the Google Drive API, allowing the generation of datasets containing images and metadata in JSON format. CliniCAM delivers a unified workflow for clinical image documentation by integrating image capture, metadata annotation, and tag-based classification within a mobile interface. The system permits efficient retrieval and supports the creation of tagged datasets for secondary applications. For example, in an orthopedic consultation, a clinician can use CliniCAM to capture high-resolution images of musculoskeletal findings, such as joint deformities, surgical wounds, or traumatic injuries, directly within the app, tag the images with terms such as “osteoarthritis” or “fracture,” and enter relevant clinical annotations. These images are immediately associated with the patient record and securely stored. During follow-up visits, the clinician can quickly retrieve previous images using patient identifiers or tags to monitor disease progression. Similarly, in wound care, nurses can document wound healing over time, with images organized by anatomical site and wound type, thereby facilitating clinical decision-making and generating datasets for quality improvement or research. CliniCAM delivers a scalable and affordable solution for structured clinical image documentation. The tag-based system enables dataset generation at the point of care, addressing limitations within traditional storage systems and supporting future research, educational efforts, and artificial intelligence applications.
Solis et al. (2026) studied this question.