Building Disease-Specific Predictive Algorithms for Targeted Disease Detection

Movement disorders are often underdiagnosed, with patients experiencing symptoms for years before receiving an accurate diagnosis. Early detection is critical to improving outcomes and quality of life, yet clinical assessment of movement disorders requires specialized expertise that isn't available in all healthcare settings. Traditional screening approaches lack sensitivity and fail to identify many individuals who could benefit from treatment. This global pharma company recognized an opportunity to leverage video-based AI to democratize early detection.

The company developed a novel, condition-specific AI algorithm trained to detect subtle motor patterns and behavioral markers characteristic of the movement disorder. The algorithm analyzed video of patients performing simple, standardized movements and tasks, using computer vision and machine learning to identify individuals at elevated risk. The algorithm was rigorously validated against clinical gold standards to ensure accuracy and reliability. Once validated, the company built a scalable platform that could be deployed across diverse settings—from clinical research sites to direct-to-consumer campaigns.

The results demonstrated the power of AI-driven disease detection. The AI-based screener identified significantly more at-risk individuals compared to traditional methods, expanding the pool of potential patients who could be diagnosed and treated. Among individuals flagged by the AI screener, diagnosis timelines were accelerated compared to standard care pathways. Most importantly, prescription rates for appropriate treatments were substantially higher among individuals identified through the AI screener, indicating that the algorithm was successfully identifying patients who would genuinely benefit from therapy. The scalable framework enabled the organization to conduct targeted disease detection campaigns across both clinical research and commercial channels.