Someone Used Claude Code to Get a Second Opinion on Their MRI — and the AI Disagreed with the Doctor. Here Is the Service Business That Opens Up.

What Happened

A developer named Antoine had right shoulder pain for two to three weeks. His doctor diagnosed a Grade III partial-thickness tear of his subscapularis tendon — more than fifty percent of the tendon width — at the apical insertion.

He did not accept the diagnosis without question. He took his MRI files — 266MB of DICOM images — and fed them into Claude Code running Opus 4.8.

The AI came back with a different reading: intact tendon, no discrete tear.

He then ran an arbitration prompt. The result: "Evidence favours Reader A (moderate-to-high confidence). Mild insertional tendinosis; NO discrete partial- or full-thickness tear identified."

The AI also flagged that his prescribed treatments had questionable clinical backing — shockwave therapy is typically indicated when calcification is present, and Traumeel is a homeopathic injection with no established therapeutic indication in peer-reviewed literature.

The story hit the top of Hacker News with 276 points.

Antoine's own conclusion: "My hope is that in a couple of model generations, we'll trust AI to review MRIs the way we trust it to proofread our emails."

He is probably right about the trajectory. The question for today is not the future — it is what you can build right now using the exact workflow he described.

Why This Is a Business, Not Just a Trick

What Antoine did required:

• Knowing what Claude Code is and how to run it
• Understanding that DICOM files exist and how to access them
• Writing prompts structured enough to extract clinical terminology and flag treatment logic
• Running an arbitration pass to weigh conflicting readings
• Knowing what the output meant well enough to act on it

Almost no patients can do that. But almost every patient who gets a complex diagnosis wants exactly what Antoine got: an independent read on what the imaging actually shows and whether the treatment plan makes sense.

That gap is the business.

The Service

Name it clearly. AI Medical Record Review. Not AI diagnosis, not AI second opinion — AI-assisted document analysis, presented as questions to ask your doctor.

What it delivers: A structured report that surfaces the key terminology in the imaging document, flags any language that suggests uncertainty or conflict with common clinical patterns, notes any prescribed treatments that appear inconsistent with the findings described, and generates a list of questions the patient can bring to their next appointment.

What it does not do: Diagnose anything. Recommend treatment. Replace a physician. That positioning is not a legal hedge — it is the accurate description of what the output actually is.

The Workflow

Claude Code with Opus 4.8 is the technical core Antoine already validated. The wrapper you build is:

1. A simple upload interface for radiology reports (PDF or text) and optionally DICOM files for tech-forward users

2. A structured prompt that extracts findings, flags clinical terminology, and asks the arbitration question Antoine ran

3. A formatted output document the patient can print and bring to their appointment

4. A follow-up prompt pass that generates questions the patient should ask

The code path for DICOM files requires pydicom and some preprocessing — Claude Code handles that natively because it can install libraries mid-session. For most users, the text-based radiology report is enough.

Pricing and Acquisition

Start with a $49 single-report review. This is the natural test — someone just got a diagnosis they do not fully understand, they are already spending money on appointments and treatment, and $49 is nothing compared to a surgical procedure they might not need.

Monthly subscription at $299 makes sense for patients with chronic conditions: autoimmune disease, cancer follow-up, orthopedic recovery, neurological conditions. These patients receive imaging or labs every few weeks and want a consistent analysis layer.

Acquisition lives in the communities where patients are already doing this manually:

• Chronic illness subreddits (r/MultipleSclerosis, r/ChronicPain, r/AutoimmuneDisease)
• Patient advocacy group forums
• Rare disease communities
• Orthopedic recovery Facebook groups

These communities already share radiology reports with each other and ask strangers with no medical training to help interpret them. You are offering a better version of something they are already doing.

The Legal Fence

The positioning Antoine used is the right one: "a second opinion," "questions to ask your doctor," "document analysis." Not diagnosis. Not medical advice.

The output should be explicitly framed as an AI-generated analysis for informational purposes. Include a visible disclaimer. Make the question list the feature — it frames the output as preparation for a conversation with a physician, not a replacement for one.

This is not a loophole. It is the accurate description of what the product does. An AI reading a DICOM file and flagging a terminology conflict is doing document analysis. That is the product.

What Makes This Work Now

Twelve months ago, feeding 266MB of DICOM files into a model and getting a clinically coherent reading was not reliably possible. It is now, as Antoine demonstrated on a public blog post that hit the front page of Hacker News.

The experiment has been done. The technical feasibility has been shown. The demand signal is in the comment section of that post, where patients with their own unresolved diagnostic questions recognized exactly what Antoine built.

The business is the translation layer between that experiment and the people who want it but cannot run it themselves.

Source: https://antoine.fi/mri-analysis-using-claude-code-opus