Pain phenotyping using artificial intelligence for the personalization of analgesic interventions: a scoping review

Pain represents one of the main health problems at the level world due to its high prevalence , complexity clinical and profound impact on quality of life , functionality and welfare psychological well-being of those who suffer from it (1,2). In this In that sense , the estimates contemporary disease burden they have shown that multiple conditions painful contribute in a way substantial to global disability , reinforcing he character priority of pain as health problem public (1). In this context , the pain management continues being a challenge major clinical despite the expansion of alternatives pharmacological interventions minimally invasive procedures and approaches multimodal , which evidence limitations persistent in the actual effectiveness of the care models . Therefore, it has been recognized that the approaches traditional pain assessment and treatment methods , focused mostly in diagnoses broad classifications anatomical scales one- dimensional intensities , result insufficient to capture heterogeneity inherent in the painful experience (3,4). In this In context , the pain must be understood as a multidimensional phenomenon that emerges from the interaction dynamics between mechanisms nociceptive , neuropathic and nociplastic , modulated by factors neurobiological , genetic , psychological , social , and contextual factors (3,5). As a consequence , it is observed a marked interindividual variability in presentation clinical , in the temporal trajectory and in the response to interventions analgesics , which limits the effectiveness of strategies therapeutic standardized and favors results suboptimal in proportions relevant patients (6) . In this scenario , the need to move forward toward one more analgesic precise has boosted he interest through pain phenotyping , understood such as the identification of subgroups of patients with characteristics clinical , sensory , neurobiological or psychosocial shared , with the potential to improve risk stratification and guide decisions therapeutic further individualized (7). In this context , has been described that processes such as central awareness and reorganization functional and structural aspects of the system highly strung they can contribute to profiles clinical differentials and variations relevant in the answer therapeutic (4,8). It has also been documented that the differences individual in sensitivity to pain and in modulation mechanisms descending are associated with trajectories clinics heterogeneous , which reinforces the need for frameworks that allow characterize subpopulations with greater granularity (9). However , despite his The conceptual promise of robust and reproducible identification of pain phenotypes raises challenges methodological significant . In this context , the approaches analytical conventional usually introduce limitations for integrating multiple data dimensions in a way simultaneous , especially when clinical variables are combined , measurements sensory information psychometric , signals physiological and neuroimaging data (10). Therefore, it is required he use of tools capable of detecting patterns complexes , nonlinear relationships and structures latent in datasets multidimensional , in order to approximate more It is precise to the real heterogeneity of pain. In this In this context , artificial intelligence has emerged as a a tool with the potential to transform research and practice pain clinic , by allowing he analysis of large volumes of information and the identification of patterns that may not be obvious through methods traditional (11,12). Therefore , learning techniques machine learning and deep learning have been applied to support patient stratification and outcome prediction relevant and the personalization of interventions analgesics , especially when they are integrated sources various as records clinical electronics , tests sensory quantitative signals physiological and neuroimaging (11,13). In a way supplementary , works classics in pain neuroscience has evidenced that there are signatures brain and processing networks associated with the perception and modulation of pain, which provides a foundation biological for approaches based in data and models computational (14,15). However, despite the growth sustained publications about artificial intelligence applied to pain, the available evidence on pain phenotyping through artificial intelligence for the purpose of personalizing interventions analgesics remains Dispersed and heterogeneous . In this context , there are variations important in the populations studied , in the types of algorithms used , in the incorporated variables and in the environments clinical evaluated , what It makes interpretation difficult comparison and translation into practice clinical . In addition , they persist questions critics related to validity , generalizability , interpretability clinical and real-world utility for decision - making , which underlines the need for a synthesis structured . Therefore, it turns out necessary carry out a scoping review that allow map in a way systematic evidence existing on the phenotyping of pain through artificial intelligence focused on the personalization of interventions analgesics . In this context , a synthesis wide will allow identify approaches methodological predominant , data sources , relevant variables , types of phenotypes proposed interventions customized derived from these models and main knowledge gaps , in order to guide investigation future and accelerate he development of precision analgesia strategies based in artificial intelligence .