AI strategy without execution is just a slide deck. I own both, through to revenue. The frameworks below aren't theoretical. Every one has been stress-tested in production clinical environments.
How I Think About Clinical AI
Most AI failures in healthcare are not model failures. They are strategy failures: wrong problem, wrong data, wrong governance, wrong moment in the workflow.
Getting it right requires a framework that holds across the full lifecycle: from problem selection through model deployment, post-market monitoring, and change control. In regulated clinical environments, that framework is not optional. It is the product.
Frameworks I Work With
Every build is governed by a structured set of decision layers:
Systematic evaluation of AI approaches against clinical, regulatory, and operational requirements before a line of code is written.
Defines where and how clinician oversight is embedded. Not a toggle. A tiered architecture calibrated to clinical risk.
Gates deployment on clinical evidence quality. Real-world validation, not benchmark performance.
Data quality as a prerequisite, not an afterthought. Structured checkpoints before model training and before deployment.
Domain-specific clinical context embedded into model design, training data curation, and output interpretation.
Post-market surveillance built in from day one. Drift detection, revalidation triggers, and structured change management.
Advisory
I work selectively with early-stage healthcare and health tech companies on AI strategy, product development, and responsible deployment in regulated clinical environments.
If you are building AI in oncology and need a strategic framework that holds up in practice, not just in pitch decks, that is the conversation worth having.