Practice Innovations
Healthcare organizations face significant challenges in utilizing conversational artificial intelligence (AI) due to privacy concerns, integration complexities, and task-specific demands1. Addressing these issues, we introduce a Large Language Model (LLM) based framework* designed for advanced, personalized wound management. The framework* distinguishes itself by its ability to adapt to institute-specific guidelines and policies, ensuring tailored guidance following local best practices. This innovation underscores the potential for AI to transform wound care through customized interventions.
Methods:
The conversational AI framework* integrates key components to deliver tailored wound management solutions (see Fig. 1). Inputs, ranging from user queries to images, feed into a dynamic AI engine powered by Retrieval Augmented Generation (RAG) and prompt orchestration for accurate information extraction while mitigating AI-hallucination. The AI engine meticulously processes inputs and extracts relevant information from user-specific libraries—comprising hospital protocols, systematic reviews, and multimedia resources—to formulate accurate responses such as treatment recommendations or alerts.
We developed two applications using our conversational AI framework: one in partnership with a leading wound care education provider2, and another integrated within an industry leading digital wound management platform**. This integration facilitates chatbot interactions and personalized treatment suggestions based on user-specific content. To ensure privacy and scalability, the system operates on a protected cloud-based architecture.
Results:
Initial testing of the applications has shown encouraging outcomes, with the proposed framework* effectively providing contextually relevant and precise information aligned with specialized wound care protocols (see Fig. 2). In the forthcoming phase, a selected cohort of wound care experts will conduct an intensive assessment of the framework* focusing on its practical application in various clinical scenarios. This targeted evaluation aims to enhance accuracy, user experience, and the framework's adaptability to various wound management cases.
Discussion:
The conversational AI framework* represents a significant advancement in personalized wound care, allowing clinicians to upload and interact with their own curated knowledge bases, thereby facilitating customized patient care plans at the fingertip of caregivers. It aims to standardize the application of wound treatment protocols across different users, ensuring a high level of consistency in care delivery. Ultimately, it seeks to elevate the standard of patient-centric wound care.