Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research.

OBJECTIVE: The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. METHODS: A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. RESULTS: After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. CONCLUSION: The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. SIGNIFICANCE: This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Persistent and Emergent Clinical Sequelae of Mild COVID-19.

BACKGROUND: Knowledge of the clinical course and consequences of COVID-19 initially evolved in the context of severe presentations and among those with comorbidities. However, understanding the outcomes of milder infections in healthy individuals is important for safe return-to-duty in extreme environments or to occupations requiring significant fitness. We reviewed the literature to characterize the nature and timing of persistent and emergent clinical sequelae in milder COVID-19 cases to facilitate development of post-COVID-19 screening and surveillance protocols.METHODS: We searched databases including EMBASE, MEDLINE, Cochrane COVID-19 study register, gray literature, clinical trial registries, and relevant health and disease prevention sources for publications from 2019 to February 18th, 2021, documenting COVID-19 sequelae. Articles were included if the COVID-19 severity was mild and there were no, or only minor, pre-existing comorbidities. Persistent and emergent sequelae were then stratified based on time since diagnosis.RESULTS: Among those with mild COVID-19, sequelae were shown to emerge or persist for months following presumed recovery. Among those with no comorbidities, cardiac, hematological, and respiratory sequelae emerged after 1-2 mo, and primarily cardiac abnormalities persisted at ≥ 3 mo. Among those with minor comorbidities, persistent respiratory abnormalities, fatigue, dyspnea, and headache were common, and mental health symptoms emerged by 1-2 mo postinfection.DISCUSSION: After presumed recovery from mild COVID-19, a range of symptoms can persist and later emerge. Whether these are new or previously unrecognized is unclear. Under-recognized COVID-19 sequelae may increase the risk of subtle or sudden incapacitation and have implications for return-to-work (RTW) screening and surveillance for safety-critical roles.Tucci V, Saary J. Persistent and emergent clinical sequelae of mild COVID-19. Aerosp Med Hum Perform. 2021; 92(12):962-969.