Pain is a universal human experience, yet it remains one of medicine’s most elusive puzzles. Historically, quantifying pain has been deeply subjective, relying heavily on self-reporting or observational scales that can fall short, particularly for those unable to communicate their distress. This inherent subjectivity has led to undetected suffering, misdiagnoses, and suboptimal care for countless individuals. However, a profound shift is underway, driven by the rapid advancements in artificial intelligence. AI is beginning to transform how we understand, assess, and ultimately manage pain, moving us towards a future where suffering might be quantified as reliably as blood pressure.
The Enduring Challenge of Subjective Pain
The very nature of pain makes it incredibly difficult to measure objectively. What feels like a “7 out of 10” to one person might be a “4” to another, influenced by a complex interplay of biology, psychology, and even culture. Traditional methods have struggled to capture this nuanced reality, leaving significant gaps in care, especially for vulnerable populations.
For years at Orchard Care Homes, a 23‑facility dementia-care chain in northern England, Cheryl Baird watched nurses fill out the Abbey Pain Scale, an observational methodology used to evaluate pain in those who can’t communicate verbally. Baird, a former nurse who was then the facility’s director of quality, describes it as “a tick‑box exercise where people weren’t truly considering pain indicators.”
As a result, agitated residents were assumed to have behavioral issues, since the scale does not always differentiate well between pain and other forms of suffering or distress. They were often prescribed psychotropic sedatives, while the pain itself went untreated. This highlights a critical flaw in purely subjective pain assessment systems, where the inability to articulate pain leads to misinterpretation and inadequate treatment.
Beyond communication barriers, personal biases can also skew pain management. Studies show that factors like gender and race can influence how pain scores are recorded and how treatment is administered, even when the reported intensity is the same. This systemic issue often leaves a significant portion of patients, particularly in intensive care units, with unrecognized or undertreated pain due to impaired communication.
AI’s Breakthrough in Objective Pain Assessment
The limitations of subjective pain measurement have fueled a fervent search for better, more objective solutions. Enter artificial intelligence. AI’s ability to analyze vast datasets and detect subtle patterns is now being harnessed to create innovative tools that can quantify pain with unprecedented accuracy.
Then, in January 2021, Orchard Care Homes began a trial of PainChek, a smartphone app that scans a resident’s face for microscopic muscle movements and uses artificial intelligence to output an expected pain score. Within weeks, the pilot unit saw fewer prescriptions and had calmer corridors. “We immediately saw the benefits: ease of use, accuracy, and identifying pain that wouldn’t have been spotted using the old scale,” Baird recalls.
In nursing homes, neonatal units, and ICU wards, researchers are racing to turn pain into something a camera or sensor can score as reliably as blood pressure. This kind of technology-assisted diagnosis hints at a bigger trend. The push has already produced PainChek, which has been cleared by regulators on three continents and has logged more than 10 million pain assessments. Other startups are beginning to make similar inroads in care settings. The way we assess pain may finally be shifting, but when algorithms measure our suffering, does that change the way we understand and treat it?
Researchers are pursuing two primary routes for AI pain quantification. The first involves listening underneath the skin, utilizing electrophysiology. Devices equipped with electrode nets look for neural signatures that correlate with pain stimuli, while others combine EEG with galvanic skin response and heart-rate variability to create a multisignal “pain fingerprint.” An example is Medasense’s PMD-200 patient monitor, which uses AI-based tools and physiological patterns to adjust pain management during surgery, leading to lower post-operative pain scores without increased opioid use.
The second path is behavioral, focusing on observable signs of distress. Computer-vision teams feed high-speed video of patients’ changing expressions into neural networks trained on the Face Action Coding System (FACS). This system, essentially a Rosetta stone of 44 facial micro-movements, allows AI models to flag pain-indicating frames with over 90% accuracy. Similar approaches analyze posture and even scan clinical notes for specific phrases using natural language processing to detect hidden pain indicators.
PainChek is one of these behavioral models, and it acts like a camera‑based thermometer, but for pain: A care worker opens the app and holds a phone 30 centimeters from a person’s face. For three seconds, a neural network looks for nine particular microscopic movements—upper‑lip raise, brow pinch, cheek tension, and so on—that research has linked most strongly to pain. Then the screen flashes a score of 0 to 42. “There’s a catalogue of ‘action‑unit codes’—facial expressions common to all humans. Nine of those are associated with pain,” explains Kreshnik Hoti, a senior research scientist with PainChek and a co-inventor of the device. This system is built directly on the foundation of FACS. After the scan, the app walks the user through a yes‑or‑no checklist of other signs, like groaning, “guarding,” and sleep disruption, and stores the result on a cloud dashboard that can show trends.
Linking the scan to a human‑filled checklist was, Hoti admits, a late design choice. “Initially, we thought AI should automate everything, but now we see [that] hybrid use—AI plus human input—is our major strength,” he says. Care aides, not nurses, complete most assessments, freeing clinicians to act on the data rather than gather it.
PainChek was cleared by Australia’s Therapeutic Goods Administration in 2017, and national rollout funding from Canberra helped embed it in hundreds of nursing homes in the country. The system has also won authorization in the UK—where expansion began just before covid-19 started spreading and resumed as lockdowns eased—and in Canada and New Zealand, which are running pilot programs. In the US, it’s currently awaiting an FDA decision. Company‑wide data show “about a 25% drop in antipsychotic use and, in Scotland, a 42% reduction in falls,” Hoti says.
Orchard Care Homes is one of its early adopters. Baird, then the facility’s director of quality, remembers the pre‑AI routine that was largely done “to prove compliance,” she says. PainChek added an algorithm to that workflow, and the hybrid approach has paid off. Orchard’s internal study of four care homes tracked monthly pain scores, behavioral incidents, and prescriptions. Within weeks, psychotropic scripts fell and residents’ behavior calmed. The ripple effects went beyond pharmacy tallies. Residents who had skipped meals because of undetected dental pain “began eating again,” Baird notes, and “those who were isolated due to pain began socializing.”
Inside Orchard facilities, a cultural shift is underway. When Baird trained new staff, she likened pain “to measuring blood pressure or oxygen,” she says. “We wouldn’t guess those, so why guess pain?” The analogy lands, but getting people fully on board is still a slog. Some nurses insist their clinical judgment is enough; others balk at another login and audit trail. “The sector has been slow to adopt technology, but it’s changing,” Baird says. That’s helped by the fact that administering a full Abbey Pain Scale takes 20 minutes, while a PainChek scan and checklist take less than five.
Engineers at PainChek are now adapting the code for the very youngest patients. PainChek Infant targets babies under one year, whose grimaces flicker faster than adults’. The algorithm, retrained on neonatal faces, detects six validated facial action units based on the well-established Baby Facial Action Coding System. PainChek Infant is starting limited testing in Australia while the company pursues a separate regulatory pathway.
The Science Behind How We Experience Pain
Understanding the fundamental biology of pain is crucial for appreciating how AI can contribute to its quantification. Pain is not a simple stimulus-response; it’s a dynamic negotiation between the body and the brain, a process that AI aims to interpret through its external manifestations.
Science already understands certain aspects of pain. We know that when you stub your toe, for example, microscopic alarm bells called nociceptors send electrical impulses toward your spinal cord on “express” wires, delivering the first stab of pain, while a slower convoy follows with the dull throb that lingers. At the spinal cord, the signal meets a microscopic switchboard scientists call the gate. Flood that gate with friendly touches—say, by rubbing the bruise—or let the brain return an instruction born of panic or calm, and the gate might muffle or magnify the message before you even become aware of it.
The gate can either let pain signals pass through or block them, depending on other nerve activity and instructions from your brain. Only the signals that succeed in getting past this gate travel up to your brain’s sensory map to help locate the damage, while others branch out to emotion centers that decide how bad it feels. Within milliseconds, those same hubs in the brain shoot fresh orders back down the line, releasing built-in painkillers or stoking the alarm. In other words, pain isn’t a straightforward translation of damage or sensation but a live negotiation between the body and the brain.
Despite these insights, much about pain remains a mystery. Scientists still grapple with predicting the transition from acute injury to chronic hypersensitivity or explaining the perplexing phenomenon of phantom-limb pain. AI’s role, therefore, isn’t to replace this complex biological understanding but to provide a consistent, data-driven interpretation of the outward signs of this internal negotiation, offering an objective lens into a profoundly subjective experience.
Navigating the Future of Pain Quantification
As promising as AI pain assessment technologies are, they are not without their complexities and potential challenges. Skepticism naturally arises, prompting important questions about accuracy, bias, and the human element in care.
Skeptics raise familiar red flags about these devices. Facial‑analysis AI has a history of skin‑tone bias, for example. Facial analysis may also misread grimaces stemming from nausea or fear. The tool is only as good as the yes‑or‑no answers that follow the scan; sloppy data entry can skew results in either direction. Results lack the broader clinical and interpersonal context a caregiver is likely to have from interacting with individual patients regularly and understanding their medical history. It’s also possible that clinicians might defer too strongly to the algorithm, over-relying on outside judgment and eroding their own.
These valid concerns underscore the importance of a hybrid approach, where AI complements, rather than replaces, human judgment and empathy. The goal is to empower caregivers with better data, freeing them to focus on personalized care and intervention, rather than spending valuable time on subjective assessments.
The landscape of pain measurement technology is continually evolving. Beyond facial analysis apps, we are seeing EEG headbands for neuropathic pain, galvanic skin sensors for breakthrough cancer pain, and language models that comb clinical notes for signs of hidden distress. This multi-pronged approach reflects the multifaceted nature of pain itself.
For Baird, the issue is fairly straightforward nonetheless. “I’ve lived with chronic pain and had a hard time getting people to believe me. [PainChek] would have made a huge difference,” she says. If artificial intelligence can give silent sufferers a numerical voice—and make clinicians listen—then adding one more line to the vital‑sign chart might be worth the screen time.
Conclusion
The journey to quantify pain, medicine’s most subjective vital sign, is reaching a pivotal moment with the advent of artificial intelligence. By leveraging physiological signals and subtle behavioral cues, AI-powered tools are offering a new, more objective lens into human suffering. This evolution promises to revolutionize pain management, especially for those unable to articulate their discomfort, leading to more accurate diagnoses, better-tailored treatments, and ultimately, a higher quality of life for countless patients.
While challenges and ethical considerations remain, the potential of AI to transform healthcare, from nursing homes to neonatal units, is undeniable. As these technologies continue to mature and integrate into clinical practice, we move closer to a future where every patient’s pain is seen, understood, and effectively addressed, fostering a more compassionate and data-driven approach to care.
