Depending on the implantation, an AI-augmented medical imaging workflow can lead to improvements in outcomes and productivity.
Promote Better Clinical Outcomes
• AI model can be trained to detect early sign of diseases
• AI acts as 2nd peer AI-reader to improve accuracy by flagging out discrepancies between human-reader
• Different AI models can review the same images and co-relate with medical records to detect multiple conditions
• AI acts as 1st read for workload prioritization to improve the time to treat
• AI can work 24x7
• AI can highlight abnormal cases to allow clinicians to focus on more complex tasks and decisions
• AI can be used as an audit tool to sample imaging read by junior radiologists
• AI can be used as a training tool for junior radiologists
Examples of Use CasesHere are two key examples of current projects which would benefit from being deployed on the AI-enabled Medical Imaging Platform.
• An AI-enabled predictive model for Community-Acquired Pneumonia
• The Singapore Eye LEsioN Analyzer (SELENA+), as part of Singapore’s Integrated Diabetic Retinopathy Programme (SiDRP), performs automated analysis of retinal images to detect diabetic retinopathy.
With the ongoing COVID-19 pandemic, there are demands to leverage AI technology to support the healthcare system in its fight against the
disease. This includes COVID-19 models from Alibaba and BioMind, and a pneumonia model by NCID-A*Star.