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Using AI to Check a Health Claim, Safely

A step-by-step method to use AI for locating evidence while guarding against false certainty and invented citations.

By Zoltán Varga2026-11-054 min read

What AI Assistants Are Good and Bad At Here

AI language models excel at retrieving known facts and summarizing existing literature quickly. They can scan thousands of documents to find patterns that a human reader might miss in a short time. This speed makes them useful for gathering initial information on a health topic or identifying relevant studies.

These systems struggle significantly with verifying truth because they predict the next word in a sequence rather than accessing a database of facts. As noted by Arvind Narayanan in his 2024 book AI Snake Oil, these models are trained to produce plausible text, not true statements. They often sound convincing even when the underlying information is incorrect.

A common failure mode involves the invention of sources. Models may cite non-existent papers, authors, or journals with high confidence. Olivier Caelen documented instances where AI claimed to access specific links it could not reach, leading users to believe the system had capabilities it lacks. You must assume any citation is fake until you prove it exists.

The core limitation is the absence of a source of truth during the training process. Even if developers filtered training data to include only accurate statements, the model would still generate errors due to its probabilistic nature. You cannot rely on the AI to distinguish between a verified medical guideline and a popular myth without external checks.

Prompts That Ask for Evidence Levels

To mitigate hallucinations, you must force the AI to classify the certainty of its own statements. Use a prompt that requires the model to tag every sentence with an epistemic status. This taxonomy includes categories like Observation for measured data, Reported Fact for cited sources, and Speculation for uncertain conjectures.

A robust prompt asks the AI to separate facts from interpretations explicitly. Facts must have source attribution or be directly observable, while interpretations must acknowledge uncertainty. This separation prevents the model from presenting a hypothesis as a proven mechanism. It forces the system to admit when it is guessing rather than stating a fact.

You should also instruct the AI to identify confidence markers such as 'may,' 'suggests,' or 'demonstrates.' These linguistic cues reveal the strength of the claim. A statement using 'demonstrates' implies high certainty, whereas 'may' signals low confidence. Tracking these markers helps you gauge the reliability of the information provided.

The goal is to transform the AI from a narrator into an analyst. Instead of asking 'Is this true?', ask 'What is the epistemic status of this claim?' This shift changes the output from a definitive answer to a structured breakdown of evidence quality. It gives you the raw material needed for your own verification.

Verifying Every Cited Source

Never accept a citation generated by an AI without checking the original document. Search for the paper title, author, and journal in a trusted database like PubMed or Google Scholar. If the paper does not exist, the entire argument based on that citation collapses immediately.

Check the date and context of the source to ensure it applies to your specific question. A study from twenty years ago may have been superseded by newer research. An intervention tested on mice may not apply to humans. Verify that the population, setting, and outcome measures match your inquiry.

Look for contradictions between different sources. One study might report a positive effect while another finds no result. Use the AI to identify these discrepancies, but rely on your judgment to weigh the quality of the evidence. A small, poorly designed study should carry less weight than a large, randomized trial.

If the AI claims to have accessed a specific URL, verify that the link works and contains the content described. Models often fabricate links that look real but lead nowhere. This behavior is a known flaw where the system simulates access it does not possess. Treat every link as a potential fabrication until confirmed.

Calibrated Conclusions

Synthesize the verified evidence into a conclusion that reflects the actual certainty level. Use frameworks like GRADE to assign a confidence rating of High, Moderate, Low, or Very Low. This rating depends on factors like risk of bias, inconsistency, and sample size.

Avoid mixing confidence levels with probability statements. Do not say 'High confidence that X is likely.' Instead, state 'High confidence that X occurs' or 'X is likely to occur.' This distinction keeps the assessment clear and prevents logical confusion about the strength of the evidence.

Consider what would change your mind about the conclusion. Define specific conditions that would overturn the current finding, such as a large-scale trial showing no effect. This approach, inspired by falsification principles, keeps your belief flexible and ready to update when new data arrives.

Document the key assumptions underlying the conclusion. If an assumption proves false, the entire conclusion may be invalid. For example, assuming a drug works the same way in all populations is a critical assumption that requires verification. Listing these assumptions highlights where further research is needed.

When to Ask a Professional Instead

Stop using AI for health decisions when the stakes involve personal treatment or diagnosis. AI cannot replace the clinical judgment of a qualified healthcare provider who knows your full medical history. Use AI for information gathering, not for making medical choices.

Seek a professional if the AI output reveals significant contradictions or low-quality evidence. If the evidence is rated as Very Low or if studies are inconsistent, a doctor can help interpret what this means for your specific situation. They can navigate the nuances that a generic summary misses.

Do not use AI to determine dosages, protocols, or treatment stacks. These decisions require precise calculation and safety monitoring that AI models are not equipped to handle. Even if the notes contain specific numbers, never follow them as a protocol. Always consult a licensed practitioner for actionable medical advice.

Remember that this guide is for educational purposes and research literacy. It is not medical advice. The information provided here helps you read health claims critically, but it does not constitute a diagnosis or treatment plan. Your health decisions should always involve a human professional.

AI-assisted: drafted with a language model from the author's research notes, then edited and checked before publication. Author: Zoltán Varga, AI visibility researcher and AI ecosystem architect, Budapest.