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Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.
RESUMO
BACKGROUND: Erector spinae plane block (ESPB) and serratus anterior plane block (SAPB) are regional anesthesia techniques that have shown favorable results in pain management following thoracic surgeries; however, their relative superiority is unclear. This review (PROSPERO: CRD42023443018) aims to compare the analgesic efficacy of ESPB and SAPB in patients undergoing thoracic surgeries through the pooled analysis of co-primary outcomes: postoperative oral-morphine-equivalent (mg) consumption in 24 h and pain scores (static) at 24 h. METHODS: A literature search was conducted across PubMed, Cochrane Library, and Google Scholar to identify randomized controlled trials (RCTs) from inception to May 2023, comparing ESPB and SAPB in thoracic surgeries. Statistical pooling was done using Review Manager 5.4.1. Bias assessment employed the Cochrane Collaboration Risk-of-Bias 2.0 tool. The strength of evidence was assessed using the guidelines from the GRADE working group. RESULTS: Nine RCTs (485 patients) were included in the study. Postoperative pain scores (static) at 24 h (mean difference (MD) = - 0.31 [- 0.57, 0.05], p = 0.02) and postoperative oral-morphine-equivalent (mg) consumption in 24 h (MD = - 19.73 [- 25.65, - 13.80], p < 0.00001) were significantly lower in the ESBP group. However, the MDs did not exceed the set threshold for clinical importance. No significant differences were observed in the opioid-related adverse effects and block-related complications. CONCLUSION: Our statistically significant results imply that ESPB has superior analgesic efficacy compared to SAPB; however, this difference is clinically unimportant. The safety profile of the two blocks is comparable; hence, current evidence cannot define the relative superiority of one block over the other. Our findings warrant further research with standardized methodologies and a longer duration of analgesic efficacy assessment to yield robust evidence for better clinical applications.
