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AbstractIntroduction: Anesthesia has evolved from enormously from mysterious sleep to intelligent, personalized care and as Artificial intelligence (AI) has led to transformation of healthcare, its applications in anesthesia too has proven of late that how technology and human compassion working together can be smoother, personalized, safer and precise surgical journeys for patients. The AI is field of computer science which use various algorithms and computational resources enabling the machines to simulate human intelligence in its thought process and actions. This revolutionary technology is changing the traditional anesthesia practices of patient risk stratification, anesthesia delivery system, patient peri-operative monitoring, individualized anesthesia plans and image analysis by an automated process. However, the application of AI in anesthesia comes with its own unique limitations of limited data and its quality, lack of guidelines for ethical use, legal concerns and socioeconomic concerns. Therefore, in this review we searched human in loop approach of the hybrid models, as the future of anesthesia will likely to be shaped by a combination of artificial intelligence and human expertise rather than one replacing other. Methods: We searched PubMed, google scholar and Cochrane database search 2019-2024. The evaluation of the retrieved articles was done for effectiveness as well as limitations of AI and deep learning in the field of anesthesia. Results: The AI will enhance precision, safety and efficiency but human touch of anesthesiologist and ethical use of it will remain essential for decision making, handling complications and providing personalized empathic care. Nonetheless, its effectiveness is influenced by data quality, algorithm generalizability and the absence of standardized ethical frameworks. Conclusion: The future of anaesthesia will likely combine AI’s analytical power with anaesthetist’s expertise, ethical judgement and empathy. While AI can improve outcomes and efficiency, the human touch remains essential for decision making, complication management and personalised care.
Suggested Citation
Handle:
RePEc:dbk:ethaic:v:4:y:2025:i::p:414:id:414
DOI: 10.56294/ai2025414
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