Table 2.

Data extraction of included studies

Author/year/country	Purpose	Study design	Sample				Methods	Major results
			Setting	Diagnosis	N	Age (years)
Type 1: AI-based approach related to the pain assessment
Lucey/2011/USA	To describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain.	Diagnostic study	Community	Shoulder pain	25	NA	Automatically detecting pain in video through facial action units	AAM can be used to analyze facial movement in videos compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only
Kharghanian/2016/Iran	To propose a new method for continuous pain detection	Diagnostic study	Community	Shoulder pain	25	NA	A hierarchical unsupervised feature learning approach	The proposed model was tested, and they achieved near 95% for the area under receiver operating characteristic curve metric that is prominent with respect to the other reported results
Dutta/2018/India	To propose a hybrid model that allowed for efficient pain recognition	Diagnostic study	Community	Shoulder pain	22	NA	Combination of—Constrained Local Model (CLM), Active Appearance Model (AAM), Patch-Based Model, image algebra	This model contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%–55% and an improved processing time of 20%–25%
Hosseini/2022/UK	To develop a highly accurate pain intensity estimation system	Diagnostic study	Community	Shoulder pain	25	NA	Deep Convolutional Neural Networks model using the transfer learning technique, were a pretrained Deep Convolutional Neural Networks model is adopted by replacing its dense upper layers, and the model is tuned using painful facial	The experiments show our method achieves a promising improvement in terms of accuracy and performance to estimate pain intensity and outperform the-state-of-the-arts models
Behrman/2006/USA	To evaluate if artificial neural networks (ANNs) can improve upon current pain scoring systems	Diagnostic study	Pain clinic	Chronic pain of 6 months or longer	155	46.4 (21–79)	Classification of patients with pain based on neuropathic pain symptoms: Comparison of an artificial neural network against an established scoring system	The results confirm the clinical experience that groups of pain descriptors rather than single items differentiate between patients with neuropathic and nonneuropathic pain The accuracy obtained by ANN analysis was only slightly higher than that of the traditional approaches, indicating the absence of nonlinear relationships in this dataset Data analysis with ANNs provides a framework that extends what current approaches offer, especially for dynamic data, such as the rating of pain descriptors over time
Atee/2018/Australia	To describe a novel method and system of pain assessment using a combination of technologies: automated facial recognition and analysis (AFRA), smart computing, affective computing, and cloud computing (Internet of Things) for people with advanced dementia	Diagnostic study	Residential aged care facilities	Geriatric resident	74	69–98	In blind comparisons with the Abbey Pain Scale, PainChek has been clinically evaluated in aged care residents with moderate to severe dementia in two prospective observational studies. They also provided a comprehensive clinimetric analysis on the performance of the app	PainChek is a comprehensive and evidence-based pain management system. This novel approach has the potential to transform pain assessment in people who are unable to verbalize because it can be used by clinicians and carers in everyday clinical practice
Fodeh/2017/USA	To analyze unstructured narrative text data in the EHR to develop a reliable classifier that detects pain assessment in clinical notes	Diagnostic study	Department of Veterans Affairs	Patients with musculoskeletal diagnoses	92	Male mean: 68 Female mean: 58	Classifying clinical notes with pain assessment	Developed a Random forest classifier to identify clinical notes with pain assessment information
Suominen/2009/Finland	To test the hypothesize that pain assessment can be supported through human language technology	Diagnostic study	Adult long-term intensive care unit	Long-term intensive care patients	516	NA	Statistically comparing annotations of ten nursing professionals on a set of 1548 documents The aspects considered include the amount and writing style of pain-related notes, pain intensity, and given pain care	More than half of the documents contained information relevant for patients’ pain status but it was expressed usually indirectly Also, pain medication was commented as free text Although annotators’ pain intensity evaluations diverged, the substantial amount of pain-related notes encourages developing computational tools for pain assessment
Hossain/2015/Kingdom of Saudi Arabia	To better understand cloud-assisted elderly patient care	Diagnostic study	Community	NA	105	Elderly patient	The device captures participants’ speech as well as their face image, and sends to the server located in the cloud. In the server, two modalities (speech and face) are processed separately in “voice detection” and “face recognition.” Scores from these two components are fused to deliver the final decision of the person’s state Based on the decision, emergency services, regular doctors, or caregivers can be contacted	The proposed recognition system can achieve more than 95 % accuracy using five instances of cloud server, and the server can generate the response within three seconds
Umapathy/2021/India	To perform automated segmentation of facial regions from thermograms using k-means clustering algorithm and to classify the data into normal and orofacial pain categories using various machine learning classifiers To implement the convolutional neural network for classification of normal and OFP subjects which involves automated feature extraction and feature selection process	Diagnostic study	Hospital patients with orofacial pain	Patients with orofacial pain	100	NA	Facial thermograms were segmented using k-means algorithm, then statistical features were extracted and classified into normal and orofacial pain using various machine learning classifier. Further, the deep learning networks such as VGG-16 and DenseNet-121 were used for automated feature extraction and classification of facial thermograms	Computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications
Wu/2022/Taiwan	to establish the deep learning-based pain classifier based on facial expressions	Diagnostic study	Hospital	Critically ill patients	63	NA	Established both image- and video-based pain classifiers through using convolutional neural network models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks	The practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings
Mallol-Ragolta/2020/Germany	to develop new digital tools that can automatically and objectively assess pain intensity in individuals	Diagnostic study	Community	Chronic Lower Back Pain	36	NA	Curriculum learning approaches to predict the pain intensity level of individuals reported in an 11-point scale from facial expressions	The results obtained using the test partition support the use of Curriculum Learning -based approaches in the automatic prediction of pain from facial features
Type 2: AI-based approaches to pain prediction and clinical decision support
Nickerson/2016/USA	To compare the performance of conventional vs state-of-the-art machine learning techniques in predicting pain response	Cohort study	Shands Medical Center	Patients who underwent nonambulatory or nonobstetric surgery	26090	NA	Constructed a neural network based on the long short-term memory architecture and trained it on pain score patterns	Machine learning techniques may offer much benefit for developing smarter postoperative pain management strategies
Lötsch/2018/Germany	To create a simple questionnaire with good predictive power for persisting pain after surgery	Cohort study	Hospital	Women who had unilateral nonmetastasized breast cancer	1000	NA	Machine-learned predictors were first trained with the full-item set of Beck’s Depression Inventory (BDI), Spielberger’s State Trait Anxiety Inventory (STAI), and the State Trait Anger Expression Inventory (STAXI-2). Subsequently, features were selected from the questionnaires to create predictors having a reduced set of items	A combined seven-item set of 10% of the original psychological questions from STAI and BDI, provided the same predictive performance parameters as the full questionnaires for the development of persistent postsurgical pain
Honcu/2020/Czech Republic	To demonstrate the effectiveness of the diagnostic and therapeutic medical information system Computer Kinesiology in physiotherapy in patients with low back pain who were not responding to conventional therapy	Pilot study	Community	Acute and chronic back pain; healthy volunteers	173	<43.7 years: 48.8 (37.9–59.9) ≥43.7 years: 62.1 (51.0–72.3)	All subjects were examined three times by the diagnostic part of the Computer Kinesiology method	The author demonstrated a high therapeutic efficacy of the Computer Kinesiology system in patients with back pain and in persons without back pain who used the Computer Kinesiology system for primary and secondary prevention of back pain
Knab/2001/USA	To test the hypothesis that computer-based decision support (CBDS) could allow primary care physicians (PCPs) to more effectively manage patients with chronic pain	Longitudinal study	Pain Clinic	Chronic pain	50	NA	A pain specialist used a decision support system to determine appropriate pain therapy and sent letters to the referring physicians outlining these recommendations Separately, five board-certified PCPs used a CBDS system to “treat” the 50 cases Two pain specialists reviewed the PCPs’ outcomes and assigned medical appropriateness One year later, the hospital database provided information on how the actual patients’ pain was managed and the number of patients re-referred by their PCP to the pain clinic	On the basis of CBDS recommendations, the PCP subjects “prescribed” additional pain therapy in 213 of 250 evaluations (85%), with a medical appropriateness score of 5.5 ± 0.1 Only 25% of these chronic pain patients were subsequently re-referred to the pain clinic within 1 year The use of a CBDS system may improve the ability of PCPs to manage chronic pain and may also facilitate screening of consults to optimize specialist utilization
Lopez-Martinez/2019/USA	To apply reinforcement learning for the recommendation of pain management regimes and the automatic dosing of analgesics	Retrospective study	Intensive care unit	Patient with pain	6843	NA	A sequential decision-making framework for opioid dosing based on deep reinforcement learning was presented. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient’s evolving pain and physiological condition. Morphine was the focus on morphine, one of the most prescribed opioids To train and evaluate the model, Retrospective data was used from the publicly available MIMIC-3 database	Reinforcement learning may be used to aid decision-making in the intensive care setting by providing personalized pain management interventions
Shim/2021/Korea	To develop machine learning models that can accurately predict the risk of chronic lower back pain	Retrospective study	Community	Respondents who participated in the Korea National Health and Nutrition Examination Surveys	6119	64 (56–72)	Classification models with machine learning algorithms were developed and validated to predict chronic lower back pain	Machine learning could be effectively applied in the identification of populations at high risk of chronic lower back pain
Hao/2022/China	To investigate use of multidata analysis based on an artificial neural network (ANN) to predict long-term pain outcomes after microvascular decompression in patients with trigeminal neuralgia and to explore key predictors	Retrospective study	Hospital	Patients with trigeminal neuralgia	1041	53.6 ± 10.2	Multidata analysis based on an ANN to predict long-term pain outcomes	The ANN model, constructed using multiple data, predicted long-term pain prognosis after microvascular decompression in patients with trigeminal neuralgia objectively and accurately. The model was able to assess the importance of each factor in the prediction of pain outcome
Guan/2021/USA	To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis	Longitudinal study	Community	Subjects with or at risk of knee OA	4674	61 ± 9.2	A DL model was developed to predict pain progression using baseline knee radiographs. An artificial neural network was used to develop a traditional risk assessment model to predict pain progression using demographic, clinical, and radiographic risk factors	DL models using baseline knee radiographs had higher diagnostic performance for predicting pain progression than traditional models using demographic, clinical, and radiographic risk factors
Gao/2021/China	To evaluate the accuracy of back propagation artificial neural network model for predicting postoperative pain following root canal treatment	Cohort study	Hospital	Patients who received root canal treatment	300	≤20: 0; 20–30: 0.25; 30–40: 0.5; 40–60: 0.75; ≥60: 1	Neural network model was trained and tested	Back propagation network model could be used to predict postoperative pain following root canal treatment and showed clinical feasibility and application value
Goldstein/2020/USA	To develop a mobile platform for tracking pain patients’ emotions, cliexa-EASE, which allows patients to self-report BSMs of emotional states, pain, stress and fatigue in a user-friendly and engaging way	Cohort study	Community	Chronic pain	84	43.23 ± 15.68	Developed a mobile platform for measuring pain, emotions, and associated bodily feelings in chronic pain patients in their daily life conditions	The best predictors of future pain were interactive effects of body maps of fatigue with negative affect and positive affect with past pain
Type 3: AI-based approach related to the pain self-management
Sandal/2020/Denmark	To investigate the basis for recruitment and screening procedures for the subsequent randomized controlled trial To test the inclusion process in relation to questionnaires and app installation	Pilot study	Primary care clinic	Low back pain within the past 8 weeks	51	45.5 ± 15	Use the selfBACK app for 6 weeks The app provided weekly tailored self-management plans targeting physical activity, strength and flexibility exercises, and education	The primary outcome Roland-Morris Disability Questionnaire improved from 8.6 at baseline to 5.9 at 6-week follow-up Participants spent on average 134 min (range 0–889 min) using the app during the 6-week period
Sandal/2021/Denmark	To investigate the effectiveness of selfBACK app, an evidence-based, individually tailored self-management support system delivered via an app as an adjunct to usual care for adults	Randomized clinical trial	Primary care clinic	Low back pain within the past 8 weeks	461	47.5 ± 14.7	Use the selfBACK app for 6 weeks The app provided weekly tailored self-management plans targeting physical activity, strength and flexibility exercises, and education	The percentage of participants who reported a score improvement of at least 4 points on the Roland-Morris Disability Questionnaire was 52% in the intervention group vs 39% in the control group The improvement in pain-related disability was small and of uncertain clinical significance
Rabbi/2018/USA	To determine whether the MyBehaviorCBP recommendations were perceived as easy and actionable compared to randomly generated recommendations To examine preliminary evidence to see whether the intentions led to an actual increase in physical activity behavior To Solicit participant feedback on using the app to fine-tune future versions of the app	Pilot study	Wellness Center and retiree	Chronic back pain (≥6 months in duration)	10	31–60	A week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition In the next 2 weeks, MyBehaviorCBP recommendations were issued An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities	MyBehaviorCBP’s automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow MyBehaviorCBP recommendations were actualized more with an increase in approximately 5 min of further walking per day compared to the control
Lo/2018/China	To investigate the self-perceived benefits of an AI-embedded mobile app to self-manage chronic neck and back pain	Observational study	Active users of the specific AI-embedded mobile app	Neck and low back pain within the past 3 months	161	18–25: n = 30 26–30: n = 31 31–40: n = 56 41–50: n = 19 51–60: n = 21	Active users of the specific AI-embedded mobile app user was invited to participant the study The evaluation questionnaire included 14 questions that were intended to explore if using the AI rehabilitation system may (1) increase time spent on therapeutic exercise, (2) affect pain level (assessed by the 0–10 Numerical Pain Rating Scale), and (3) reduce the need for other interventions	An increase in time spent on therapeutic exercise per day was observed The median Numerical Pain Rating Scale scores were 6 before and 4 after using the AI-embedded mobile app. A 3-point reduction was reported by the participants who used the AI-embedded mobile app for more than 6 months Reduction in the usage of other interventions while using the AI-embedded mobile app was also reported
Huang/2011/USA	To present a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment	Observational study	Centre for Pain Services	Chronic pain	187	NA	Four different feature selection methods were applied to rank the questions Four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance	There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or area under the receiver operating characteristic curve (AUC); however, there were significant differences between the classifiers for each ranking method The multilayer perceptron classifier had the best classification performance on an optimized subset of questions, which consisted of ten questions. Its overall classification accuracy and AUC were 100% and 1, respectively
Meheli/2022/USA	To evaluate the perceived needs of users with chronic pain conditions To evaluate the app engagement and disengagement patterns of users with chronic pain	Observational study	Community	Chronic pain	2194	NA	The users voluntarily downloaded the Cognitive Behavioral Therapy-Based Artificial Intelligence Mental Health App and completed the questionnaires	The findings indicate that users look for tools that can help them address their concerns related to mental health, pain management, and sleep issues The study findings also indicate the breadth of the needs of users with chronic pain and the lack of support structures, and suggest that Wysa can provide effective support to bridge the gap
Piette/2022/USA	To determine if a CBT-CP program that personalizes patient treatment using reinforcement learning and interactive voice response (IVR) calls is noninferior to standard telephone CBT-CP and saves therapist time	Randomized clinical trial	Community	Patients with chronic back pain	278	63.9 ± 12.2	All patients received 10 weeks of CBT-CP. For the AI-CBT-CP group, patient feedback via daily IVR calls was used by the AI engine to make weekly recommendations for either a 45- or 15-min therapist-delivered telephone session or an individualized IVR-delivered therapist message. Patients in the comparison group were offered 10 therapist-delivered telephone CBT-CP sessions (45 min/session)	The findings of this randomized comparative effectiveness trial indicated that AI-CBT-CP was noninferior to therapist-delivered telephone CBT-CP and required substantially less therapist time
Anan/2021/Japan	To evaluate the improvements in musculoskeletal symptoms in workers with neck/shoulder stiffness/pain and low back pain after the use of an exercise-based AI -assisted interactive health promotion system that operates through a mobile messaging app (the AI-assisted health program)	Two-armed, randomized, controlled, and unblinded trial	Community	Workers with neck/shoulder pain/stiffness	94	41.8 ± 8.7	Intervention group received the AI-assisted health program, in which the chatbot sent messages to users with the exercise instructions at a fixed time every day through the smartphone’s chatting app (LINE) for 12 weeks	This study shows that the short exercises provided by the AI-assisted health program improved both neck/shoulder pain/stiffness and low back pain in 12 weeks

Abbreviations: CBT-CP: cognitive behavioral therapy for chronic pain; AI: artificial intelligence; NA: not applicable.