Artificial Intelligence
Developing responsible AI endpoints, data methods, and evaluation standards for NF clinical trials.
Leadership
About This Group
The REiNS group is developing and evaluating artificial intelligence (AI) methods to strengthen clinical research endpoints by improving reliability, sensitivity to change, and feasibility of measurement across studies. Across disease areas (NF1 and NF2-SWN) and functional domains, the goal is to use AI to extract clinically meaningful signals from complex data (for example, images, video, audio, sensor streams, and text), reduce unwanted variability, and support consistent scoring and interpretation while preserving clinical intent.
Current group leader: Frank Buono
Data governance, privacy, and security are essential, and model documentation should enable auditability and reproducibility. Potential uses for specific REiNS working groups include: · For cutaneous and image-based evaluations (e.g., MRI), AI can support objective and repeatable characterization of visible phenotypes, lesion or rash extent, morphology, and change over time, while improving standardization of image capture and scoring. · For functional outcomes, AI can quantify movement, performance, and behavior from wearable sensors and video, potentially producing sensitive measures aligned with meaningful daily activities and function. · For neurocognitive and communication-related outcomes, AI can characterize speech, language, and cognitive performance from structured tasks and naturalistic samples, supporting consistent assessment and longitudinal tracking. · For patient-reported outcomes, AI can help summarize and structure patient-reported information and other patient-generated data, improving signal extraction while maintaining interpretability and alignment with what matters to patients.
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Membership is open to all interested parties. Meetings are primarily held by teleconference.
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