Healthcare Data Partnerships to AI-Ready Assets

Healthcare AI programs depend on much more than model development. The hard part is often deciding which labeled and unlabeled data is worth pursuing, how to structure collaboration, and how to make the resulting assets reusable, governed, and fit for training and evaluation.

My work in this area includes:

• Prioritizing data acquisition, annotation, and platform investments by clinical-domain fit, downstream model value, partner readiness, technical feasibility, fit-for-purpose criteria, regulatory constraints, cost, and reuse potential.
• Driving data access paths, data-use agreements/SOWs, contribution standards, licensing decisions, vendor/tooling selection, active-learning and annotation operations, pricing and staged-pilot paths, and release/reuse planning.
• Aligning data contributors, clinical experts, data stewards, platform engineers, AI/algorithm teams, regulatory stakeholders, and business leaders around durable data products.
• Establishing public-safe operating patterns for de-identification, provenance, access, quality, data lifecycle ownership, and governance.

The goal is to turn collaborations into data assets that can feed multiple programs over time: product AI, algorithm validation, real-world evidence, post-market surveillance, clinical informatics, and future multimodal/physical-AI workflows.