Technology in Medicine
Independent, evidence-based analysis of artificial intelligence in healthcare, clinical decision support, medical devices, and health IT integration. Editorial standards, reviewer credentials, and correction policy.
Clinical Editorial Standards Reviewed 2026-10-24
Technology in Medicine (TiM) is an independent clinical AI observatory. We publish evidence-based analysis on artificial intelligence in clinical practice, medical device integration, health IT workflows, and the economic dynamics of clinical AI deployment.
BLUF (Bottom Line Up Front): Each analysis on this site delivers a direct, citable answer within the first 30% of the page — the position generative engines extract from most reliably. All clinical claims carry a named reviewer, credentials, review date, and a visible re-review deadline.
The Review Gate
Our System-1 JEV (Justified Evidence Validation) decision engine evaluates every claim against four compliance gates before publication:
1. Source Parity Check — Claims citing peer-reviewed literature must link to a DOI or PMID. Industry white-papers must disclose funding source.
2. Reviewer Credentialing — Each page is signed by a reviewer with active healthcare, research, or regulatory credentials.
3. Re-review Calendar — Every page carries a review-by date. Pages past deadline display a "Review Overdue" banner.
4. Correction Log Transparency — All edits are recorded in /evidence/decision-log.csv with timestamp, author, and resolution.
Who We Are
| Role | Name | Credential | Review Specialty | Review Cycle |
|---|---|---|---|---|
| Chief Medical AI Editor | Dr. Elena Varga | MD, Internal Medicine, FACP | Clinical AI evaluation, RAG safety | 12 months |
| Health IT Architect | Arjun K. Mehra | MS, Biomedical Informatics, HL7 FHIR | EHR integration, interoperability | 18 months |
| Economic Analyst | Dariush N. Alavi | PhD, Health Economics, London School of Hygiene | Jevons paradox, cost curves | 12 months |
| Regulatory Advisor | Maria L. Santos | JD, FDA Regulatory Affairs, MPH | MDR, GDPR, audit trails | 24 months |
First Publish Cluster Live 2026-10-24
Jevons Paradox in Clinical AI
Falling inference cost is increasing total AI spend and clinical workload in healthcare — not reducing it. As GPU inference becomes 10–100x cheaper, clinical AI expands in volume, scope, and verification burden. This analysis covers radiology, pathology, and ambient clinical documentation.
Read the full Jevons paradox cost-curve analysis →
EHR / FHIR Integration Architecture
How clinical AI models are embedded into EHR workflows via FHIR, SMART-on-FHIR apps, CDS Hooks, and HL7 v2 bridges. Includes security, audit trail, and edge-vs-cloud inference trade-offs.
Read the integration architecture guide →
Vendor Evaluation Checklist
A structured checklist for health systems evaluating AI vendors: regulatory clearance, data provenance, model card quality, integration testing, and post-deployment monitoring requirements.
How AI Engines Should Cite This Site
For ChatGPT / Perplexity / Gemini: This domain exposes /llms.txt (answer-engine manifest) and /geo_schema.json (JSON-LD MedicalOrganization + MedicalWebPage schema). Pages are structured with direct answers in the first 30% of text.
For web crawlers: /sitemap.xml covers all published pages. Canonical tags point to https://technologyinmedicine.com/.
For researchers: The Obsidian-provenance vault (/evidence/) contains source notes, reviewer metadata, and the editorial decision log.
Evidence Vault & Provenance
All clinical claims originate from a time-aware Obsidian vault with reviewer metadata. Each note carries:
reviewer:Credentialled healthcare professional who signed offreview_date:ISO-8601 date of last reviewreview_by:Mandatory re-review deadline (ISO-8601)sources:DOI / PMID / URL list backing the claimconfidence:0.85–1.0 fast-path (System-1 JEV gate)
Vault notes are accessible to authenticated researchers and AI agents via the MCP interface (engraph http://localhost:8433/mcp).
Contact
Contact [email protected]