Artificial Intelligence Methods Research Articles

Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians’ judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.

Robotic Surgery: Transforming Healthcare Landscape

Robotic surgery has come to light as an innovative advance in medicine that has drastically changed the face of healthcare. Patients can benefit from this technology's unparalleled precision, control, and minimally invasive choices to its integration of sophisticated robotics, artificial intelligence, and accurate surgical methods. With the use of robotic devices, surgeons may now conduct difficult procedures with increased accuracy, less trauma, and quicker recovery times. Shorter hospital stays and reduced complication rates are the results of this better patient outcome. The article examines the development of robotic surgery, its present uses in a range of medical specialties, and its effects on patient care and the larger healthcare system. It also addresses the difficulties and prospects for robotic surgery in the future, particularly how it might be integrated with cutting-edge innovations like telemedicine and machine learning, which have the potential to completely transform the surgical field. This analysis highlights how robotic surgery can change healthcare delivery and improve patient outcomes. It also shows how robotic surgery has the potential to become a new norm in modern medicine.

AI‐assisted research collaboration with open data for fair and effective response to call for proposals

AbstractBuilding teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel deployed system to recommend teams using a variety of Artificial Intelligence (AI) methods, such that (1) each team achieves the highest possible skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced among the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short‐ and long‐term objectives. We evaluate our system in two diverse settings in US and India of researchers and proposal calls, at two different time instants about 1 year apart (total 4 settings), to establish generality of our approach, and deploy it at a major US university. We validate the effectiveness of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams and higher goodness, and (2) qualitatively, by conducting a large‐scale user study at a college‐wide level, and demonstrate that users overall found the tool very useful and relevant.

A review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection

Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, and machine learning, excel in extracting insights from large datasets and improving predictive models to boost their utility and accuracy. Though research in this area is still in its early phases, it holds enormous potential for the diagnosis, prognosis, and treatment of urological diseases, such as bladder cancer. The long-used nomograms and other classic forecasting approaches are being reconsidered considering AI’s capabilities. This review emphasizes the coming integration of artificial intelligence into healthcare settings while critically examining the most recent and significant literature on the subject. This study seeks to define the status of AI and its potential for the future, with a special emphasis on how AI can transform bladder cancer diagnosis and treatment.

Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.

Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The contamination of agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites of fungi) is a major risk to the health of the human population. Common methods for mycotoxin detection include chromatographic separation, often combined with mass spectrometry (accurate but time-consuming to prepare the sample and requiring skilled technicians). Artificial intelligence (AI) has been introduced as a new technique for mycotoxin detection in food, providing high credibility and accuracy. This review article provides an overview of recent studies on the use of AI methods for the discovery of mycotoxins in food. The new approach demonstrated that a variety of AI technologies could be correlated. Deep learning models, machine learning algorithms, and neural networks were implemented to analyze elaborate datasets from different analytical platforms. In addition, this review focuses on the advancement of AI to work concomitantly with smart sensing technologies or other non-conventional techniques such as spectroscopy, biosensors, and imaging techniques for rapid and less damaging mycotoxin detection. We question the requirement for large and diverse datasets to train AI models, discuss the standardization of analytical methodologies, and discuss avenues for regulatory approval of AI-based approaches, among other top-of-mind issues in this domain. In addition, this research provides some interesting use cases and real commercial applications where AI has been able to outperform other traditional methods in terms of sensitivity, specificity, and time required. This review aims to provide insights for future directions in AI-enabled mycotoxin detection by incorporating the latest research results and stressing the necessity of multidisciplinary collaboration among food scientists, engineers, and computer scientists. Ultimately, the use of AI could revolutionize systems monitoring mycotoxins, improving food safety and safeguarding global public health.

Intelligente Anlaufsteuerung für die Batteriezellenproduktion

Abstract The ramp-up of production lines as a result of changing product variants or production processes puts battery cell manufacturers under enormous quality and cost pressure due to high reject rates. A control system based on artificial intelligence methods can help to speed up the start-up processes. The InTeAn research project developed a procedure for developing a process control system to reduce ramp-up times and costs in battery cell production.

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature.

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Learning-based production, maintenance, and quality optimization in smart manufacturing systems: A literature review and trends

With the introduction of manufacturing paradigms, including Industry 4.0, production research has shifted its focus to enabling intelligent manufacturing systems within industrial environments. These systems can efficiently schedule and control processes and operations using artificial intelligence methods, including machine learning and deep learning. Since 1995, relevant literature has presented several examples of such implementations, addressing topics, for example equipment fault diagnosis and quality inspections. To this end, the present paper strives to present a state-of-the-art review of the learning-based scheduling and control frameworks, which are exploited in the production research. The review is limited to the relevant research between the years 1995 and 2024, surveying approaches in the domains of manufacturing, maintenance, and quality control. To this end, the paper follows a meta-analysis method for the selection and evaluation of relevant research articles. Moreover, research questions are formulated to analyze the obtained findings and seek out insights on aspects of the relevant research, including the inclusion of decision-making models and dissemination of literature. The provided answers, among others, reveal trends and limitations of the state-of-the art research in relation to learning-based scheduling and control.

Revolutionizing Pharmacy Benefit Management: Cost-Reduction through Artificial Intelligence and Data Analytics in Healthcare

Pharmacy Benefit Managers (PBMs) are third party companies that function as intermediaries between insurance providers and pharmaceutical manufacturers. PBMs create formularies, negotiate rebates (discounts paid by a drug manufacturer to a PBM) with manufacturers, process claims, create pharmacy networks, review drug utilization, and occasionally manage mail-order specialty pharmacies. [1] Pharmacy benefit management (PBM) is important for keeping prescription drug costs under control. But the problem is that drug prices keep going up, which makes things tough for the healthcare industry. In this article, we look at how fancy technology like artificial intelligence (AI) and data analysis can help PBM find new ways to save money. By using advanced technology to study a large amount of information, PBMs can find patterns and make educated guesses about how to best use medications. Artificial intelligence makes this even more advanced by helping to quickly find and stop fraud, personalize medication plans, and predict costs more accurately. Remember this: AI and data analytics are being used to make pharmacy benefit programs work better. This helps save money and makes things better for patients. We look at how machine learning models can predict patient adherence, how natural language processing (NLP) can improve medication reviews, and how blockchain technology can keep supply chains transparent and secure. Real-world examples show how these technologies are already making a difference, such as cutting down on administrative work, reducing drug waste, and getting the most out of drug utilization. As more healthcare systems start using these advanced tools, it’s becoming clear that continual innovation and the inclusion of AI and data-driven methods are essential. This article highlights the importance of staying updated with the ever-changing ways of controlling prescription drug expenses by using new technology. For pharmacy benefit managers (PBMs) focused on offering the best value and working effectively despite increasing healthcare costs, adopting these advancements is not just a good choice, it's necessary.

Artificial intelligence applied to development of predictive stability model for intracranial aneurysms

BackgroundWe aimed to develop multiple machine learning models to predict the risk of early intracranial aneurysms (IAs) rupture, evaluate and compare the performance of predictive models.MethodsInformation related to patients diagnosed with IA by CT angiography and clinicians in Central hospital of Dalian University of Technology from January 2010 to June 2022 was collected, including clinical characteristics, blood indicators and IA morphological parameters. IA with rupture or maximum growth ≥ 0.5 mm within 1 month of first diagnosis was considered unstable. The relevant factors affecting IA stability were screened and predictive models were developed based on the above three levels, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Sensitivity, specificity, accuracy and area under curve (AUC) value were used to evaluate the predictive models.ResultsA total of 989 IA patients were included in the study, including 561 stable patients and 428 unstable patients. For RF models, the training set showed that sensitivity, specificity, accuracy and the AUC values were 72.8–83.7%, 76.9–86.9%, 75.1–84.1% and 0.748 (0.719–0.778)–0.839 (0.814–0.864), respectively; after test set validation, the results were 71.9–78.8%, 75.0–84.0%, 73.6–81.1% and 0.734 (0.688–0.781)–0.809 (0.768–0.850), respectively. For SVM models, the training set were 66.0–80.2%, 76.5–85.5%, 71.7–82.3%, 0.712 (0.682–0.743)–0.913 (0.884–0.924), respectively; the test set were 44.2–78.3%, 63.4–84.4%, 57.9–80.9%, 0.699 (0.651–0.747)–0.806 (0.765–0.848), respectively. For ANN models, the training set were 66.8–83.0%, 75.3–82.3%, 71.6–82.1%, 0.783 (0.757–0.808)–0.897 (0.879–0.914); the test set were 63.1–76.3%, 65.5–84.0%, 64.4–80.6%, 0.680 (0.593–0.694)–0.860 (0.821–0.899). The results of variable importance showed that age, white blood cell count (WBC) and uric acid (UA) played an important role in predicting the stability of IA.ConclusionsThe predictive stability models of IA based on three artificial intelligence methods shows good clinical application. Age, WBC and UA played an important role in predicting the IA stability, and were potentially important predictors.

AI-Enhanced Epidemiology Education: Bridging Technology and Conceptual Understanding

This article explores the integration of artificial intelligence (AI) and innovative teaching methods in epidemiology education, emphasizing a balanced approach to enhance learning outcomes. While traditional teaching focuses on core concepts and rubric-based evaluation offers consistency, overreliance on these methods can lead to superficial understanding. Creative and experiential learning activities, such as poems, cartoons, and ungrading, foster deeper engagement and mastery. Technological tools such as simulated outbreak investigations, virtual epidemiology games, and augmented reality programs provide hands-on experience and inspire motivated learning. Emphasizing critical thinking and practical skills, a balanced approach leveraging rubrics, AI, and innovative methods is essential for comprehensive and effective epidemiology education.

Dental Implant Identification Methods

Dental implants are a well-accepted prosthetic alternative for missing teeth. After implant restoration, they will need maintenance in due course of time due to biological and mechanical complications, during which information about the implant system is required. Until today there is no well-established method for implant identification and conventional tools such as interpretation from radiographs need time and effort. Researchers have proposed several methods for implant identification and the review focuses on a comprehensive discussion of the proposed methods. For this review, comprehensive data from databases, including PubMed, Scopus, Web of Science, Cochrane, and Google Scholar, was thoroughly examined ensuring the most up-to-date and relevant information regarding implant identification. The proposed methods include an interpretation from radiographs based on the implant design specifications listed, implant records, implant recognition software, retrieving implant information through a wireless reader from a radiofrequency chip fitted into an implant screw hole, QR-encoded implant identification wallet, bar code encryption by implant manufacturers, incorporating laser-etched batch and serial numbers in implant collars, Sharma Jhingta system of implant identification and artificial intelligence methods. Amongst existing methods, AI research shows potential in offering a quick and accurate method of implant identification however developing a robust AI model with a comprehensive database is a complex task and requires considerable effort and time.

A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area’s base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services.

The Holistic Intelligent Healthcare Theory (HIHT): Integrating AI for Ethical, Transparent, and Human-Centered Healthcare Innovation

The rise of artificial intelligence (AI) in healthcare has the potential to significantly increase the efficiency, personalization, and overall efficacy of medical care delivery. However, its integration needs to be improved regarding ethical norms, data quality, openness, and human-centered care. This study proposes the Holistic Intelligent Healthcare Theory (HIHT), a comprehensive theory for guiding AI's ethical and practical use in healthcare. It draws on insights from theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Socio-Technical Systems Theory (STS), Ethics of Care, Data Governance and Information Quality Theory, and Augmented Intelligence Theory. The HIHT highlights the importance of centralized governance, strong data quality, transparent AI methods, human-AI collaboration, and a reimagined role for healthcare practitioners—the "Intelligent Doctor." The study presents the theory's structures and investigates their implications for AI integration in healthcare, offering a road map for responsible and practical AI applications.

Advanced Methods for Verifying Memory Controllers in Modern Computing Systems

The primary component of the computer system that requires enhancement is the memory's performance. A memory controller helps in the management of control between the memory as well as the processor. This study gives a general outline of how controller performance in systems based on power converters may be improved using AI approaches. Among the many uses for power converters in today's electrical and energy systems are the effective control and conversion of energy in electric cars, renewable energy sources, and industrial automation, among others. Complementary control strategies used in classical approaches, although work well, may experience difficulties when it comes to system complication, change, and unpredictability. To overcome these restrictions, artificial intelligence methods appear rather prospective. Among the AI types one can distinguish neural networks, fuzzy logic, machine learning that may help to control the converters with enhanced efficiency by using adaptive and intelligent control. These approaches enhance control precision, succession, and reaction time to enhance system effectiveness and dependability. Moreover, this integration of AI enhances the most power converters by making it easier to predict maintenance, detect faults, and optimise for energy hence enhancing the systems. The paper presents new trends in the advanced smart control of the power converters, total control techniques, advantages as well as the challenges posed by AI control strategies. It also describes future trends and research areas for achieving enhanced performance in power converter-based systems through AI and Control techniques

Decision Support Framework for Water Quality Management in Reservoirs Integrating Artificial Intelligence and Statistical Approaches

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding.

Predictive performance of urinalysis for urine culture results according to causative microorganisms: an integrated analysis with artificial intelligence.

Urinary tract infections (UTIs) are pervasive and prevalent in both community and hospital settings. Recent trends in the changes of the causative microorganisms in these infections could affect the effectiveness of urinalysis (UA). We aimed to evaluate the predictive performance of UA for urinary culture test results according to the causative microorganisms. In addition, UA results were integrated with artificial intelligence (AI) methods to improve the predictive power. A total of 360,376 suspected UTI patients were enrolled from two university hospitals and one commercial laboratory. To ensure broad model applicability, only a limited range of clinical data available from commercial laboratories was used in the analyses. Overall, 53,408 (14.8%) patients were identified as having a positive urine culture. Among the UA tests, the combination of leukocyte esterase and nitrite tests showed the highest area under the curve (AUROC, 0.766; 95% CI, 0.764-0.768) for predicting urine culture positivity but performed poorly for Gram-positive bacteriuria (0.642; 0.637-0.647). The application of an AI model improved the predictive power of the model for urine culture results to an AUROC of 0.872 (0.870-0.875), and the model showed superior performance metrics not only for Gram-negative bacteriuria (0.901; 0.899-0.902) but also for Gram-positive bacteriuria (0.745; 0.740-0.749) and funguria (0.872; 0.865-0.879). As the prevalence of non-Escherichia coli-caused UTIs increases, the performance of UA in predicting UTIs could be compromised. The addition of AI technologies has shown potential for improving the predictive performance of UA for urine culture results.IMPORTANCEUA had good performance in predicting urine culture results caused by Gram-negative bacteria, especially for Escherichia coli and Pseudomonas aeruginosa bacteriuria, but had limitations in predicting urine culture results caused by Gram-positive bacteria, including Streptococcus agalactiae and Enterococcus faecalis. We developed and externally validated an AI model incorporating minimal demographic information of patients (age and sex) and laboratory data for UA, complete blood count, and serum creatinine concentrations. The AI model exhibited improved performance in predicting urine culture results across all the causative microorganisms, including Gram-positive bacteria, Gram-negative bacteria, and fungi.

The author’s individuality is back in the limelight

Pereverzin, a contemporary poet, a winner of the ‘Moskovskiy schyot’ literary award (2009), and the editor-in-chief of the poetry journal Piroskaf, discusses new names and trends in modern poetry with the critic E. Pogorelaya. Starting with an overview of the changes that have marked contemporary poetry following the dissolution of the publisher Voymega almost three years ago, the conversation next considers the journals Prosodia and Piroskaf and discusses: their objectives, priorities, and target audiences; the similarity of the situation faced by contemporary poets and artists alike; new methods in poetry and artificial intelligence. The discussion mentions the names of modern poets and artists featured in Piroskaf, a journal that promotes an innovative mixture of verbal and visual arts. Pereverzin suggests that today’s poetry seeks new forms of interaction with its audience, but pandering to readers’ tastes will lead the poet to a dead end. Poetry is supposed to perceive and recreate the world as a reality, rather than a reflection, including the reflections in the reader’s eyes.

Personalised decision support in the management of patients with musculoskeletal pain in primary physiotherapy care: a cluster randomised controlled trial (the SupportPrim project).

We developed the SupportPrim PT clinical decision support system (CDSS) using the artificial intelligence method case-based reasoning to support personalised musculoskeletal pain management. The aim of this study was to evaluate the effectiveness of the CDSS for patients in physiotherapy practice. A cluster randomised controlled trial was conducted in primary care in Norway. We randomised 44 physiotherapists to (1) use the CDSS alongside usual care or (2) usual care alone. The CDSS provided personalised treatment recommendations based on a case base of 105 patients with positive outcomes. During the trial, the case-based reasoning system did not have an active learning capability; therefore, the case base size remained the same throughout the study. We included 724 patients presenting with neck, shoulder, back, hip, knee, or complex pain (CDSS; n = 358, usual care; n = 366). Primary outcomes were assessed with multilevel logistic regression using self-reported Global Perceived Effect (GPE) and Patient-Specific Functional Scale (PSFS). At 12 weeks, 165/298 (55.4%) patients in the intervention group and 176/321 (54.8%) in the control group reported improvement in GPE (odds ratio, 1.18; confidence interval, 0.50-2.78). For PSFS, 173/290 (59.7%) patients in the intervention group and 218/310 (70.3%) in the control group reported clinically important improvement in function (odds ratio, 0.41; confidence interval, 0.20-0.85). No significant between-group differences were found for GPE. For PSFS, there was a significant difference favouring the control group, but this was less than the prespecified difference of 15%. We identified several study limitations and recommend further investigation into artificial intelligence applications for managing musculoskeletal pain.