Machine learning vs. bodily pain: 5 points of promise and challenge

We have seen the future of pain management, and it is in automated assessments aided by AI.

That’s a paraphrase of the conclusion made by computer-science researchers who reviewed the literature on various modalities for automated recognition of physical pain. They concentrated on readily observable signs such as facial expressions, physiological signals, audio cues and pupil dilation.

The team also zeroed in on assessment approaches that leveraged the observational aptitude of machine learning. 

“Recent advances in sensor technology, signal processing, feature extraction and machine-learning algorithms are essential to the success of physiological signal-based automatic pain assessment,” write the authors, led by Ruijie Fang, a PhD candidate in computer engineering at UC-Davis. 

JMIR AI published the team’s report Feb. 24. Here are five illustrative excerpts on the potential of machine learning, aka “ML,” to help human pain-busters help suffering patients. 

1. As technology advances, the potential for real-time pain monitoring grows. 

Innovations in wearable technology, ML algorithms and data integration are “paving the way for ever more accurate and responsive pain-management systems,” Fang and colleagues write. More: 

‘These systems promise to transform how pain is managed in healthcare settings, making care more proactive, patient-centered and effective.’

2. Traditional ML still dominates the field of automated pain assessment. 

One possible reason for this is that its more advanced offshoot, deep learning, requires extensive data, which, the authors point out, is time-consuming and resource-intensive to collect. 

‘Studies often include only a small number of participants, typically in the tens, making it difficult to gather comprehensive datasets.’

3. Transfer learning presents a viable alternative. 

TL—which uses ML to refine a new model using a similar model’s prior gains—“addresses the challenges associated with varying data distributions and limited dataset sizes, enhancing model robustness and performance,” the authors write. 

‘Future research should explore the potential of transfer learning algorithms further, integrating them into clinical practice to improve pain management outcomes.’

4. ML models are only as good as the data they’re trained on.

“If the training data are biased, the model will be biased too,” the authors remind. “Bias can result in inaccurate pain assessment, leading to inadequate pain management and, in some cases, even harm to patients.”

‘It is crucial to ensure that the data used to train the models are representative and unbiased.’

5. Experimental pain research with healthy volunteers could be useful. 

This approach allows for strictly controlled conditions, larger participant pools, and the repeated application of pain stimuli, Fang and co-authors point out. 

‘These data are foundational to the development of ML models for automated pain detection.’

The authors call for further research focused on developing more robust algorithms and leveraging deep learning and transfer learning. 

“Continued interdisciplinary research and collaboration are key to overcoming current challenges and fully realizing these technologies’ benefits,” they write. “Collaborative efforts to create comprehensive pain datasets are crucial, as is integrating real-time pain monitoring into clinical practice.” 

Their conclusion: 

‘Automated pain assessment has the potential to transform pain management.’ 

The report is available in full for free.