Artificial Intelligence Clinical Evaluation (AiCE)
AiCE, an LTTS integrated AI tool, revolutionizes the Clinical Evaluation Report (CER) process by performing systematic review and reporting related to clinical literature, and additional databases that may be included in the CER. AiCE simplifies the three-step clinical evaluation process:
- Consumer Software Manufacturers - Identify clinical data from existing literature, clinical experience, clinical trials, or a combination of the three.
- Enterprise Software - Appraisal of the data’s relevance, applicability, quality, and significance.
- IoT Software - Articulation of the conclusions in the CER, based on the collected data.
AiCE’s cognitive capabilities provide a near-perfect solution for literature search, improving the efficiency, cost-effectiveness, speed, and accuracy of CER research. The tool minimizes bias, allows for algorithm reuse and customization, auto-translates sources in other languages, and accelerates the process of literature selection and extraction, significantly decreasing the time taken for manually searching and filtering appropriate content. AiCE automatically screens literature and highlights crucial 2-3 sentences in different methods of screening.
Introduction to AiCE (Artificial Intelligence Clinical Evaluation)
To have a transparent and objective approach for this literature review, the tool adopts a five-stage approach:
A search string based on the research questions is identified and applied to the abstract and citation database.
The titles and abstracts of the queried articles are analyzed to identify relevant articles from the results of the search string queries.
Presents the inclusion criteria applied to the articles in review (Quality criteria).
Undertaken on the whole literature data as highlighted for use in CER
The main data is extracted from the screening process and the relevant points are highlighted for evaluation.
Features
User Role-Based Access Management
Roles - Manager, Researcher, Quality Analyst Roles Supported
Dashboard And Reports
Project Creation And Workflow Management
AI-assisted literature Pre-Screening
Provision to configure device specific procedures in the project tab
Enable PICO entity recognition
Integrate with one more research database
Configure and select pre-set criteria for article inclusion and exclusion
Second pass screening on full-text articles
PRISMA chart
Benefits
Reduction in opportunities for errors by 30%
Enhanced CER approval for each equipment
Improved productivity by over 30%
Quicker, regulation-aligned decision making
Success Stories
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