Use AI to guide clinical decision making

By incorporating the latest advances in Artificial Intelligence (AI) and Deep Learning, inSight gives clinicians the power to make data-driven clinical decisions to effectively treat patients. From wound etiologies to treatment planning and prediction, learn how AI can assist.

Introducing healBot, A Personal AI Assistant for Advanced Wound Care

healBot is a conversational artificial intelligence (AI) platform that provides evidence-based guidance on clinical protocols, product qualification analysis, and reimbursement pathways for patients with various types of wounds. healBot is a powerful tool designed to help wound care professionals improve workflow efficiencies, enhance patient care, and optimize operations.

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Meet eKare Gauss™, an end-to-end AI Ecosystem

From labeling your data to automatic AI model selection and finally deploying your model in the cloud, eKare Gauss provides a one stop shop to remove the barrier of AI utilization. Gauss has access to eKare’s data lake with over 100,000 images, measurements and 3D scans. No wound data? No problem. Select data using the extensive filter possibilities and use it for your AI needs right away.

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Draw insight embedded in the visual data

Convolutional Neural Networks

Convolutional Neural Networks (CNN) are the de facto standard applying AI in computer vision and learning insight or patterns from visual data. CNN based models are used to perform image classification or wound healing prediction at eKare.

Hybrid Deep Learning Model

Usually in narrow AI, interference is based on either visual data or numerical data (e.g. patient demographics). eKare has developed hybrid models that leverage visual and demographic data. Doing so can improve model accuracy and opens up new possibilities for advanced AI utilization in health care and research.

Support Vector Machines (SVM)

Support Vector Machines (SVM) can be a light-weight and fast alternative to the more complex deep learning methods. eKare uses SVM as a way to achieve offline machine learning and on mobile devices with limited computation power.

eKare in action: Clinical Research

Maximizing patient recruitment across study sites is a critical requirement for clinical trial study teams. By using AI and big data powered intelligence, study teams can capitalize on site and subject recruitment strategies for their desired indications. With eKare’s technology, AI can be used to help optimally design the protocol and develop pertinent inclusion/exclusion criteria at minimal cost. Our technology uses deep learning to trend and predict healing rates allowing study teams to take full control over study progression.

Uncover Intelligence from both Structured and Unstructured Data

Diagnosis & Risk Stratification

Determine wound etiology and perform risk stratification with holistic data to optimize management planning.

Treatment Suggestion & Optimization

Access evidence-based expert guidelines optimized for the individual patient at the point of care.

Prognosis & Outcome Prediction

Predict and trend treatment outcomes using anomaly detection and deep learning to maximize resource allocation and client quality.

eKare in action: Healthcare Providers

Whether you’re a healthcare provider, clinical practice, or caregiver, Artificial Intelligence (AI) can be a powerful tool for diagnosis, treatment, and prognosis of wounds. Healthcare providers use eKare’s platform to query thousands of similar cases and draw insight to help clinical decision making. On a broader scale, clinical practices can optimize operations by using AI to assess resource trends and detect anomalies at individual, department, or organizational levels. Due to lack of standardization in contemporary wound care, caregivers can often find it difficult to treat a chronic wound with appropriate treatment standards. AI is used to find similar cases and provide information about successful treatment options. View our publications to learn more.