How to Evaluate Medical Claims in the Age of AI

Intro

Medical information has never been more accessible — or more confusing.

Patients today are exposed to:

Conflicting expert opinions
Viral health claims without sources
AI-generated summaries that flatten nuance
Politicised interpretations of genuine science

This guide explains how medical evidence works, how to recognise weak or misleading claims, and how to make better decisions when certainty is unavailable.

Key Points

Not all “studies” carry equal weight
Correlation is not causation
Scientific disagreement is normal — but bounded
AI and social media amplify weak signals
Better decisions depend on evidence and context

🧠 Evidence Explainer: What Counts as Scientific Evidence?

In medicine, not all “evidence” is equal.

Here’s the hierarchy that traditionally governed medical truth — even if it’s rarely explained to the public:

Strongest evidence

Randomised controlled trials (RCTs)
Large, well-designed meta-analyses
Consistent replication across populations

Moderate evidence

Observational cohort studies
Case–control studies
Biological plausibility with supporting data

Weak evidence

Case reports
Anecdotes
Correlations without controls

Not evidence

Temporal coincidence (“this happened after that”)
Hypotheses treated as conclusions
Calls to “investigate” without a defined mechanism
Claims that cannot be falsified

Science advances by discarding weak explanations, not amplifying them.

The problem in the modern information ecosystem isn’t lack of data.
It’s loss of agreement on which data should be allowed to settle questions.

Why Correlation Is So Often Misused

Many modern health scares follow the same pattern:

Two events occur around the same time
A correlation is observed
Causation is implied
The narrative spreads faster than correction

Large datasets make coincidences inevitable.

Without good controls, correlations are noise, not signals.

How to Stress-Test a Medical Claim

Before accepting or sharing a claim, ask:

What type of evidence supports this? (RCT/meta-analysis vs correlation vs anecdote)
Has it been replicated by independent groups?
Does it show causation or only association?
Are confounders addressed (age, comorbidities, socioeconomic factors, surveillance bias)?
Is uncertainty acknowledged or smoothed over?
Is the claim falsifiable (can it be proven wrong)?

If these answers aren’t clear, treat the claim as unproven.

The Role of AI in Medical Information

AI systems are powerful summarisation tools — not scientific referees.

They tend to:

Reflect existing discourse
Optimise for balance and coverage
Avoid explicit harm, but not “weak truth”

AI may accurately summarise what people say without adjudicating what deserves credibility.

That responsibility still lies with humans and institutions.

What Good Health Communication Looks Like

The most reliable guidance usually has:

Clear sourcing (primary studies, systematic reviews, official recommendations)
Transparent uncertainty (what’s known vs unknown)
Specificity (who it applies to, who it doesn’t)
Consistency over time (updates explained, not quietly swapped)

FAQ

Q: Why do experts disagree if science is solid?
A: Because science estimates risk and trade-offs. Experts may disagree on thresholds, weighting of harms, and how to act under uncertainty.

Q: Should I distrust health institutions?
A: Not by default. But trust should be conditional: look for transparency, sourcing, and honest updates.

Q: Can patients really evaluate evidence themselves?
A: You don’t need to become a researcher. Learning the basics of evidence hierarchy and red flags can prevent most bad decisions.