How AI-powered tools like PainChek, an app that scans a person's face for tiny muscle movements, are helping health care providers better assess patients' pain
For years at Orchard Care Homes, a 23‑facility dementia-care chain in northern England, Cheryl Baird watched nurses fill … X: @deenamousa , @deenamousa , @deenamousa , @deenamousa , and @deenamousa Bluesky: @folletto and @hypervisible.blacksky.app X: Deena Mousa / @deenamousa : Linking the scan to a human‑filled checklist was, they admit, a late design choice. Initially, they thought AI should automate everything but found that hybrid use yielded better results... Deena Mousa / @deenamousa : Just out in @techreview: I look at the companies using AI to measure how much pain patients are in based on everything from involuntary facial movements, to heart rate, to peripheral temperature changes. Will this oust the classic self-reported 1-10 scale? [image] Deena Mousa / @deenamousa : Data shows a ~25% drop in antipsychotic use and, in Scotland, a 42% reduction in falls from use. One clinician mentioned that residents who had skipped meals because of undetected dental pain began eating again, and those who were isolated due to pain began socializing. Deena Mousa / @deenamousa : There are several devices in clinical use today, like PainChek, a smartphone app that scans the facial expressions of people who have dementia and uses AI to output an expected pain score to inform their care. They also record data for the patient and facility over time. [image] Deena Mousa / @deenamousa : PainChek Adult was offered de novo FDA clearance this week, and engineers are now adapting the code for the very youngest patients. PainChek Infant targets babies under one year, whose grimaces flicker faster. Bluesky: Erin Casali / @folletto : One of the #1 issues today is that people express levels of pain much differently, ESPECIALLY chronic pain. — It takes really surface level research to know this. — The idea that it can be detected by looking at the face is fiction, and it's ridiculous anyone is giving it attention. [embedded post] @hypervisible.blacksky.app : “In nursing homes, neonatal units, and ICU wards, researchers are racing to turn pain—medicine's most subjective vital sign—into something a camera or sensor can score as reliably as blood pressure.”
Context & Ripple Effects
Clinical AI coverage has moved from tools that document care to tools that inform bedside assessment: clinicians were already using AI note-taking assistants, while the NHS had deployed an AI-enabled stethoscope in primary care. PainChek extends that trajectory to a hard-to-verbalize symptom in dementia care.
The article also sharpens a recurring implementation question. Rather than fully automating judgment, PainChek’s developers added a human-completed checklist after finding hybrid use performed better—a meaningful constraint amid earlier concerns that hospital decision-support systems could be novel and insufficiently proven.
First-order effects
- Care teams using PainChek can add a repeatable facial-analysis score and longitudinal record to pain assessments for patients who may struggle to self-report; the tool is explicitly paired with a human checklist rather than used as a stand-alone verdict.
- PainChek Adult’s de novo FDA clearance gives the company a regulatory foothold for its adult product, while its infant adaptation broadens the product-development focus to another nonverbal patient group.
Second-order effects
- Providers adopting facial pain scoring will need to embed it in assessment and documentation workflows, train staff on when to override it, and evaluate whether the reported associations with lower antipsychotic use and fewer falls transfer to their own settings.
- Competing clinical-AI vendors face pressure to show that their outputs improve decisions within clinician-led workflows, not merely that they can classify a signal; skepticism over whether faces reliably reveal pain raises the evidentiary bar.
Third-order effects
- If hybrid pain-assessment tools gain traction, subjective bedside observations may increasingly become structured, comparable data that can shape medication review, safety monitoring, and care-quality measurement.
- That shift remains contingent on validation across patients and settings. Disagreement over facial cues is likely to keep clinical oversight, transparent performance evaluation, and regulatory scrutiny central to this category.
The trend: Clinical AI is shifting from back-office automation toward workflow-embedded measurement tools whose value depends on human review and demonstrable patient-care outcomes.