After years of relying on checklists like the Abbey Pain Scale, Orchard Care Homes in northern England piloted PainChek, a smartphone app that uses artificial intelligence to analyze facial micro-movements and estimate a pain score. Within weeks, staff reported fewer prescriptions for psychotropic sedatives and calmer wards, with residents resuming activities like eating and socializing once hidden pain was identified. The shift reflects a broader push in nursing homes, neonatal units, and ICU wards to make pain, historically the most subjective vital sign, measurable with the consistency of blood pressure.
Traditional scales have evolved from 1920s line marks to today’s 0 to 10 ratings and adjective-rich questionnaires, yet pain remains subjective and context dependent. Expectations and emotions can alter perceived intensity, as shown by a trial where belief in a pain relief cream reduced reported pain by 22 percent and produced corresponding brain activity changes. Cultural and clinical biases further complicate treatment: identical stimuli drew higher scores from Italian women than Swedish and Saudi participants, women’s pain scores were recorded less often than men’s in a 2024 analysis, and Black children with limb fractures were less likely to receive opioid analgesics than white non-Hispanic peers, even when pain scores were similar. Many ICU patients cannot self-report, leaving pain unrecognized or undertreated.
Researchers are pursuing two technical approaches. One listens to physiological signals such as EEG, galvanic skin response, and heart-rate variability to detect neural or autonomic patterns linked to pain. A 2024 machine-learning study reported over 80 percent accuracy distinguishing chronic pain patients from controls using resting-state EEG. Medasense’s PMD-200 monitor uses artificial intelligence to track intraoperative physiology and guide anesthesia dosing; in a 2022 study of major abdominal surgery, it yielded lower postoperative pain scores, a median of 3 out of 10 versus 5 in controls, without increasing opioid use. The PMD-200 is authorized by the US Food and Drug Administration and is in use in the United States, the European Union, Canada, and other markets.
The second approach analyzes behavior. Computer vision models trained on the Face Action Coding System can flag pain-indicative expressions with accuracy approaching expert assessors, and natural-language processing scans clinical notes for telltale phrases. PainChek blends a three-second facial scan, focused on nine validated action units, with a brief caregiver checklist and a cloud dashboard for trends. Cleared in Australia in 2017 and authorized in the UK, with pilots in Canada and New Zealand, the app has logged more than 10 million assessments and company data link it to a 25 percent drop in antipsychotic use and a 42 percent reduction in falls in Scotland. Orchard staff report that the hybrid workflow is faster than legacy tools and frees nurses to act on the data.
Developers are extending the technology to infants, retraining algorithms on neonatal faces using a baby-specific coding system and beginning limited testing in Australia. Skeptics caution about known risks, including skin tone bias in facial analysis, misreading pain versus fear or nausea, the quality of checklist inputs, loss of clinical context, and over-reliance on algorithmic scores. Even so, backers argue that if artificial intelligence can surface hidden distress and give nonverbal patients a numerical voice, adding a new line to the vital sign chart could improve care while the field studies and mitigates the technology’s limits.
