AI-generated health summaries are one of the most genuinely useful applications of machine learning to patient care. They can translate dense medical language into plain English, extract the most important findings from a stack of documents, and surface patterns that no one has explicitly pointed out to you.
They also have real limits. Understanding both sides makes the technology useful rather than misleading.
What an AI health summary actually does
A health summary AI reads your uploaded records — lab results, clinic notes, prescriptions, radiology reports — and does three things: it extracts the relevant facts, it puts them in context, and it presents them in language a non-specialist can understand.
The 'context' step is where AI adds genuine value. A ferritin of 12 ng/mL in isolation is a number. An AI that knows your age, sex, reported fatigue and recent haemoglobin trend can flag it as consistent with iron deficiency and suggest discussing it with your doctor. It's connecting dots that would otherwise require you to read multiple documents and know what you're looking for.
What it's reliably good at
Summarising large volumes of text quickly. If you have fifteen years of medical documents and need to prepare for a new specialist appointment, an AI summary can distill the most relevant facts in minutes — something that would otherwise take hours.
- Plain-language explanations of lab values and clinical terms
- Flagging results that are outside normal ranges
- Identifying patterns across multiple records — e.g., a cholesterol value trending up over several years
- Generating questions you might want to ask your doctor based on the findings
- Producing a structured overview you can share with a provider
What it cannot do
An AI health summary cannot diagnose you. It can identify that a pattern of results is consistent with a particular condition — but consistency is not confirmation. Diagnosis requires clinical judgment: a physical examination, a medical history taken in conversation, and professional expertise.
AI summaries are also only as good as the data they can see. If key records haven't been uploaded, or if important context exists only in a doctor's head, the summary will be incomplete. Garbage in, garbage out — more politely stated, the summary reflects the records you've given it.
The Looms approach
Looms generates AI health overviews from your own uploaded records — lab results, prescriptions, medical reports and more. The overview includes a structured breakdown of key findings, a vitals chart where relevant, and a plain-language narrative. It's designed to help you have better conversations with your doctor, not to replace those conversations.
Every AI output is explicitly framed as informational, not clinical advice. The source records are linked so you can verify exactly what the summary is based on.
How to use it well
Treat an AI health summary as preparation, not conclusion. Read it before an appointment to refresh your understanding of your own record. Use it to identify findings you want to discuss. If something in the summary surprises or concerns you, bring the specific underlying document to your appointment and ask your doctor to explain it.
The best outcome of a good health summary is a more informed patient having a more productive conversation with a clinician — not a patient who decides they've already got their answer.