Health, wellness & care
Care is already using AI. Most teams have not talked about it yet.
Patients, doctors, nurses, families, and people building tools. Start with questions they can ask together.
- Named in care
- 41
- Highest risk
- 15
- Clinical AI
- 24
- All concerns
- 1000
Composite score · Health, Safety & Care
Most of what we named is already serious. Two sit at the top of the scale.
- 25 · irreversible & common · 2
- 20 · 13
- 16 · 24
- 15 and under · 2
Three rooms, one ecosystem
The clinic
A wrong score can harm a patient.
Triage, imaging, notes, prescribing, a robot at the table. The problem is not that AI is here. It is that it can be here without being named, checked, or owned.
- Who was this tested on, and do they look like the people we serve?
- What must remain a human judgement even if the model is faster?
- If this is wrong, is it a delay or an irreversible harm — and who is named?
- 25
Triage algorithm bias
Under-triaging minority patients.
- 20
Diagnostic errors
Missed cancers, misread scans.
- 20
AI documentation "ambient scribes"
Accuracy, hallucination, consent.
- 20
Clinical-note summarization errors
Fabricated symptoms or histories.
- 20
AI-driven prescribing
Drug-interaction and dosage recommendations.
- 20
AI in surgery (robotics)
Autonomy vs surgeon control.
The household
Companions, elders, children, a wellness app in the pocket.
Care does not only happen in a hospital. A chatbot at 2 a.m., a parent negotiating a school tool, an elder with a device that talks back: that is health too.
- When is this a tool, and when is it being asked to be a friend?
- Does the person understand what is listening, and can they refuse?
- What would I need to know before I let this sit with a child, or with someone I love who is fading?
- 20
AI companions and adolescent development
Sensitive-window disruption.
- 20
Chatbots producing self-harm content
Documented failure modes.
- 20
Chatbots on suicidality
Inconsistent crisis-safety behavior.
- 20
Pediatric AI safety
Adult-trained models mis-applied to kids.
- 20
Geriatric AI
Elderly under-represented; polypharmacy complexity.
- 16
Companion apps for the elderly
Isolation reduction vs displacement of human care.
The community
Public health, school screening, the air and water models.
A town does not buy most of the systems it lives under. It still has to ask who is scored, who is missed, and who may refuse.
- Who is flagged, who is missed, and who owns the record that follows?
- What would public accountability look like for a tool we did not purchase?
- How do we speak about benefit without hiding who is left out of the training set?
Borrowed from MIT · Causal taxonomy
Three questions that turn a worry into a sentence.
MIT asks of every risk: who set it in motion, whether it was on purpose, and when it arose. Tap a column. The example is from care, home, or community — not from a lab.
A person · By accident · Once people were using it
In the clinic
A scribe no one has time to check
An ambient note-taker is switched on to save minutes. The clinician is still responsible for the record. In a full clinic, review thins out. A fabricated symptom enters a chart.
Three questions that turn a worry into a sentence: who set it going, whether it was on purpose, and when. Walked with cases from care. Use them in a huddle. The sources page holds the denser maps when you need them.