AI Bias in Pain Management Is an ED Problem

A 2024 study from Beth Israel Deaconess Medical Center found that AI chatbots—including GPT-4 and Google's Gemini Pro—consistently underassessed pain in Black patients compared to white patients. The Gemini Pro model exhibited false beliefs about racial differences in pain perception 24% of the time. Human medical trainees scored 12%. GPT-4 came in at 9%. Every rater—human and machine—showed the same directional bias. The AI did not fix the problem. It encoded it. This same bias has been noted in human studies as well.  Who trains the AI?  Humans!

For emergency physicians, this is not an abstract ethics discussion. Pain management is one of the highest-volume clinical decisions in the ED. If the AI tools we are integrating into clinical workflows carry embedded biases around race, sex, and socioeconomic status, those biases will propagate at scale—faster and less visibly than the human biases they were supposed to replace.

The Bias Is in the Training Data

AI models learn from the data they are trained on. In pain management, that data comes overwhelmingly from electronic health records and clinical trials that overrepresent patients of European descent, underrepresent women in pain-specific research, and carry decades of documented prescribing disparities baked into the record. A 2025 narrative review published in the Journal of Pain Research laid out the problem in granular detail: AI algorithms trained on these datasets perpetuate sex stereotypes in pain detection, amplify racial inequalities in pain rating—particularly in conditions like osteoarthritis—and produce systematically different recommendations based on patient socioeconomic status.

The mechanism is straightforward. If Black patients were historically undertreated for pain in the ED—and the evidence is unambiguous that they were—then any AI model trained on that prescribing history will learn to recommend less aggressive pain management for Black patients. The model is not making a clinical judgment. It is reproducing a pattern. And it does so with the confidence and formatting of an evidence-based recommendation, which makes the bias harder to detect, not easier.

A June 2025 UCLA study confirmed what many ED physicians already suspected: Black patients presenting to the emergency department are significantly less likely to receive opioid analgesics than white patients with comparable presentations. When AI systems are trained on data that reflects this disparity, they do not correct it. They automate it.

Where This Shows Up in Your ED

The risk is not limited to opioid prescribing. Any AI-driven clinical decision support tool that touches pain assessment—triage algorithms that prioritize patients based on predicted acuity, sepsis models that factor in vital sign thresholds that may differ by demographic, documentation assistants that generate pain management plans—carries the potential for embedded bias. A 2025 study in Communications Medicine tested physicians using GPT-4 assistance in a chest pain triage scenario and found that while AI improved overall decision accuracy from 47% to 65%, the researchers specifically noted the need for ongoing vigilance about demographic bias in real-world deployment outside controlled conditions.

Consider the NarxCare algorithm, a clinical decision support tool used to assess opioid risk in prescribing decisions. A 2025 qualitative study in the Journal of General Internal Medicine examined how the opioid industry influenced the development and implementation of risk assessment algorithms like NarxCare. The concern is not that risk scoring is inherently wrong—it is that the factors feeding these scores may embed socioeconomic and racial proxies that penalize patients for their demographics rather than their clinical risk.

Emergency physicians make pain management decisions under time pressure, cognitive load, and competing demands. When an AI tool provides a recommendation that aligns with an existing unconscious bias—or when it generates a pain assessment that systematically underrates certain patient populations—the physician may accept it without interrogation. We must guard against this at all costs. This is confirmation bias amplified by algorithmic authority. The machine agrees with the heuristic, so the heuristic feels validated.

The Nuance That Matters

Not all of the research is bleak. The Communications Medicine chest pain study found that AI assistance improved physician accuracy without exacerbating demographic disparities in that specific controlled scenario—a finding that suggests well-designed AI can augment clinical decision-making equitably. But the operative phrase is "well-designed." The gap between a controlled vignette study and a 3 AM shift in a crowded ED with 30 patients on the board is enormous. The conditions that produce bias—fatigue, time pressure, incomplete information, cognitive overload—are the exact conditions under which emergency physicians are most likely to defer to algorithmic recommendations without critical evaluation.

The 2025 Journal of Pain Research review recommended specific strategies: fairness-aware techniques such as reweighting algorithms and adversarial debiasing, diverse and representative training datasets, culturally sensitive pain assessment tools, and continuous monitoring of AI outputs across demographic groups. These are sound recommendations. They are also not happening in most EDs that are currently deploying AI tools. The gap between what researchers recommend and what hospitals implement remains wide.

What You Should Be Doing Now

• First, know which AI tools in your department touch pain assessment and management decisions. This includes triage algorithms, clinical decision support platforms, documentation assistants, and opioid risk scoring tools. If you cannot identify them, you cannot evaluate them for bias.
• Second, ask your vendors directly whether their models have been validated across diverse demographic groups—specifically by race, sex, and socioeconomic status. Ask for the data. If the answer is vague or the validation was done on a homogeneous population, that is a red flag that should inform your governance decisions.
• Third, treat AI-generated pain management recommendations the same way you treat a consultant recommendation: as one input into your clinical judgment, not as a final answer. The physician remains the decision-maker. Don't every relegate your decision making capacity to AI. The algorithm is an advisor with known limitations.
• Fourth, push for institutional monitoring of AI-assisted pain management outcomes stratified by demographics. If your department is tracking patient satisfaction, time-to-analgesia, and prescribing patterns, those metrics should be analyzed by race, sex, age, and insurance status. Patterns of disparity that emerge after AI implementation deserve immediate attention.

Dr. Chet's Take
I have spent 25 years treating patients in pain in community hospitals and Level I academic emergency departments. I have also spent years directing a telemedicine network where nurse practitioners and physician assistants at remote sites make pain management decisions with physician oversight. In both settings, the question is the same: are we treating the patient in front of us, or are we treating a pattern the system taught us to see?

AI does not eliminate bias. It scales it. And in emergency medicine—where a pain management decision made under cognitive fatigue at 0300 can determine whether a patient gets relief or gets sent home undertreated—scaling bias is not a minor risk. It is a patient safety failure. In military medicine, we call this a known hazard in the operational environment. You do not ignore it. You plan for it, train against it, and build systems that catch it before it reaches the patient. That is exactly what AI governance in pain management requires.

— Dr. Chester "Chet" Shermer, MD, FACEP is a Professor of Emergency Medicine, Medical Director for Air Medical and Critical Care Transport programs, and a military medical commander with the Army National Guard. He is the founder of Global MedOps Command and the creator of AI in Emergency Medicine: Becoming AI Bulletproof.

AI Won't Wait. Neither Should You.

Bias in AI-driven pain management is one of the clinical risk domains that most emergency physicians have never been trained to evaluate. Understanding where algorithmic bias enters your workflow—and how to mitigate it—is a governance skill, not a technology skill. If you want a structured framework for navigating AI risk in the ED, consider enrolling in my course: AI in Emergency Medicine: Becoming AI Bulletproof.

Learn more at Global MedOps Command. My books on emergency department operations and AI preparedness are available on Gumroad.