●Node.js + Healthcare

Healthcare AI integration requires navigating constraints that don't exist in other industries. HIPAA restricts what data can be sent to AI providers and how. The FDA may regulate your AI feature as a medical device. Clinical accuracy requirements far exceed typical software quality standards—a hallucination could harm a patient. The architecture must accommodate these constraints from the start. De-identification is often the pragmatic starting point. Many clinical AI use cases can work without patient identifiers: summarize this clinical note (de-identified), explain this medication interaction (general question), suggest differential diagnoses for these symptoms (de-identified). This avoids PHI transmission entirely while still providing clinical value. When PHI is necessary, the minimum necessary rule requires careful selection. You can't send a full patient record to get AI suggestions—you send only the specific data elements needed. This requires understanding what the AI actually needs and implementing selection logic that enforces HIPAA's minimum necessary standard. The regulatory landscape favors Clinical Decision Support that augments rather than replaces clinical judgment. Design AI features that present information and recommendations for clinician review, not automated actions. Display evidence sources. Allow independent verification. Require explicit clinician confirmation before any action. This design pattern often qualifies for CDS exemption from FDA device regulation.

Healthcare AI projects start with understanding the clinical use case in depth. What decision is being supported? What information does the clinician need? What's the current workflow? This shapes whether AI can help and how it should be integrated. Regulatory classification comes early. I work with regulatory consultants to determine whether a proposed feature might be classified as a medical device. Often, design choices (requiring clinician review, displaying evidence) keep features in the CDS exemption category. For features involving PHI, I implement de-identification as the first processing step where clinically appropriate. The de-identified text goes to LLM providers. Results come back without patient context. Only then is the output applied to the specific patient. This pattern minimizes compliance risk while enabling AI capabilities. The RAG architecture uses validated medical sources exclusively. PubMed for research evidence, clinical guidelines for treatment recommendations, drug databases for medication information. No web scraping or unvalidated sources—clinical accuracy requires reliable inputs. Validation is more rigorous than typical software testing. I work with clinical advisors to create test cases covering clinical edge cases, contraindications, and scenarios where AI should express uncertainty. Regular evaluation against clinical benchmarks catches quality degradation. Human review of representative outputs ensures clinical appropriateness.