Thinking Through AI

How to choose an AI medical scribe: features to compare, EHR integration options, consent and BAA requirements, accuracy checks and questions to ask vendors.

Artificial general intelligence in healthcare: how AGI differs from the AI in use today, what current models can and cannot do, and where to focus now.

AI in medical imaging: detection, triage and measurement uses, how FDA clearance works and how to check it, PACS and DICOM integration, and local validation.

How AI for medical diagnosis works, where it is used, how FDA-authorized tools differ from research models, and what evidence to check before you adopt one.

Med-PaLM 2 explained: what Google built, how it scored on medical exams, how access worked through MedLM, and why MedGemma has now replaced it for developers.

Healthcare AI consulting explained: when to get outside help, what a good engagement covers, how to compare partners and the red flags to watch for.

AI adoption strategies for healthcare organizations: readiness, use-case selection, data and privacy, governance, workflow design and measuring results.

How to build HIPAA-compliant AI: when LLM providers need a BAA, data minimization, de-identification, audit controls and a reference architecture.

Practical data migration planning guide for healthcare teams. Scope, mapping, compliance, validation, cutover and rollback decisions explained end to end.