BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalization
arXiv:2605.28089v1 Announce Type: new Abstract: BuddyBench introduces a privacy-constrained multi-task benchmark for pediatric social-communication personalization. Unlike existing neurodevelopmental repositories that primarily emphasize imaging, genetics, or cross-sectional clinical phenotyping, BuddyBench links drill-level learning trajectories, standardized clinical assessments, BuddyPlan self-report, and randomized-treatment endpoints within a unified benchmark schema. BuddyBench combines two cohorts: ND-03 is an observational cohort with dense drill coverage for Tasks1-2 (n = 189), and ND
The proliferation of AI in sensitive domains necessitates new benchmarks that prioritize privacy while still enabling personalization, a critical step for ethical and effective AI development in healthcare.
BuddyBench represents a foundational step towards specialized, privacy-preserving AI development for pediatric neurodevelopment, offering a new model for handling sensitive patient data in AI applications.
The creation of a specialized, privacy-constrained benchmark with linked multi-modal data sets a new standard for AI research in pediatric social-communication, moving beyond general-purpose large language models.
- · AI developers in healthcare
- · Pediatric therapy providers
- · Families of children with neurodevelopmental differences
- · Ethical AI research organizations
- · General-purpose AI models in pediatric care
- · Data brokers with poor privacy protocols
Improved, personalized AI interventions for children with neurodevelopmental conditions become more feasible.
This benchmark could inspire similar privacy-constrained multi-task benchmarks in other sensitive medical fields, driving a wave of specialized medical AI.
The methodology may influence future regulatory frameworks for AI in healthcare, emphasizing privacy-by-design and integrated data schemas.
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Read at arXiv cs.AI