About

What is AI for Medical?

AI for Medical is the largest open database of real AI implementations in medicine. We catalog what hospitals, dental practices, pharmaceutical companies, and clinical laboratories have actually done with AI — the use case, the technology, and the measurable results — so that CMIOs, hospital administrators, dental practice owners, and clinical operations leaders can make informed decisions based on evidence, not vendor marketing.

Our Methodology

Every case study in our database goes through a structured collection and verification process. We do not fabricate data, generate synthetic results, or accept unverified claims.

Data Sources

Case studies are collected from three categories of sources:

• Vendor-published case studies — documented implementations from medical AI providers such as Qventus, Abridge, Pearl AI, SafelyYou, Insilico Medicine, and others.
• Independent research — reports from industry publications like Becker's Hospital Review, Healthcare IT News, HIMSS, Dental Economics, and Modern Healthcare that document specific deployments.
• Community contributions — case studies submitted directly by vendors and medical organizations, verified by our editorial team before publication.

Quality Levels

Each case study is assigned one of three quality levels:

• Verified — complete content with at least two quantified metrics, full taxonomy classification (medical specialty, use case, AI technology), and a traceable source.
• Contributed — submitted by a vendor or medical organization, reviewed by our team, and published with attribution.
• Scraped — programmatically collected from public sources. Contains structured data but may have shorter content sections.

Taxonomy & Classification

Every case study is classified across four dimensions: medical specialty (10 categories), use case type (14 categories), AI technology (10 categories), and company size. This standardized taxonomy enables cross-comparison across implementations and helps surface patterns — for example, which AI technologies deliver the strongest ROI for specific medical specialties.

Editorial Standards

• Metrics are reported exactly as published by the source — we do not round, extrapolate, or reinterpret results.
• Every case study links back to its original source when available.
• We distinguish between vendor-reported results and independently verified data.
• Case studies without quantifiable results are still included if they document a real implementation with a named organization.

About Us

We are a small team focused on making AI adoption in medicine more transparent and evidence-based. Our background spans clinical operations, data engineering, and health technology deployment.

Have questions, corrections, or a case study to share? Feel free to reach out.