Healthcare projects.

Complexity in Managing Extensive Research Data

The company faced significant challenges in managing and extracting relevant information from vast volumes of scientific literature, leading to inefficiencies.

Regulatory Compliance Burdens

Keeping up with regulatory requirements for new medical device approvals was time-consuming and resource-intensive.

Delayed Time-to-Market

The lengthy process of data extraction and regulatory compliance delayed the commercialization of new medical products.

Custom Templates & Research Strategy

Implemented centralized templates and protocol strategies to streamline literature search processes.

Data Extraction

Developed AI-powered extraction of performance outcomes from scientific articles whilst automatically building an audit trail by maintaining a record of where content was extracted in the article.

Intelligent Screening

Deployed AI-based screening tools to quickly identify and exclude irrelevant documents, enhancing the focus on pertinent data.

Efficiency Gains

Achieved a 26% increase in operational efficiency in 2023, with a target of 40% efficiency gain in 2024.

Regulatory Compliance

The AI models combined with a Human-in-the-loop approach build fully traceable decisions and automatically provides audit-ready documents.

Enhanced Data Extraction

The capability to extract customized endpoints from scientific articles up to 100 pages long streamlined research workflows

Manual Detection of Swelling

Detecting joint swelling manually is labor-intensive and requires specialized training.

Inconsistent Monitoring

Variability in the assessment of joint swelling among clinicians affects the consistency of patient care.

High Volume of Data

Managing and accurately analyzing a large volume of hand photographs for monitoring RA is challenging.

AI-Driven Detection

Implemented AI to analyze hand photographs and detect patterns indicative of joint swelling.

Automated Preprocessing

Developed a system to automatically crop and focus on relevant areas of the hand photos.

Metrical Analysis

Used AI to monitor changes in joint swelling over time by analyzing finger folds.

High Detection Accuracy

Achieved an accuracy of 84%, with 88% sensitivity and 75% specificity in detecting swollen joints.

Effective Monitoring

Demonstrated significant improvements in monitoring disease progression, as shown by decreased finger fold index (FFI) after treatment.

Efficiency Gains

Reduced the time needed for manual assessments and improved consistency in monitoring.

Manual Grading Inconsistency

Variability in grading hand OA among radiologists.

Time-Consuming Diagnostics

Manual interpretation of radiographs is slow and resource-intensive.

Limited Access to Expertise

Non-specialist healthcare providers need support for accurate OA diagnosis.

AI-Driven OA Detection

Used AI to detect OA in hand radiographs automatically.

Automated Grading

Developed models to classify OA severity consistently.

User-Friendly Interface

Created a web application for easy access and use by healthcare professionals.

Increased Diagnostic Accuracy

Achieved 79% sensitivity and 86% specificity for detecting OA.

Enhanced Grading Consistency

Maintained 75% accuracy in grading OA severity, reducing variability among radiologists.

Efficiency Gains

Reduced the time required for radiographic analysis, allowing radiologists to focus on more complex cases.