Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications

AI-powered techniques in anatomical imaging: Impacts on veterinary diagnostics and surgery

BACKGROUND

Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutional neural networks, show promise in interpreting medical images from various modalities like X-rays, ultrasounds, CT scans, and MRI/mammography.

STUDY DESIGN

Narrative review OBJECTIVE: This review aims to explore the innovations and challenges of AI-enabled imaging tools in veterinary diagnostics and surgery, highlighting their potential impact on diagnostic accuracy, surgical risk mitigation, and personalized veterinary healthcare.

METHODS

We reviewed recent literature on AI applications in veterinary diagnostic imaging, focusing on their benefits, limitations, and future directions.

CONCLUSION

AI-enabled imaging tools hold immense potential for revolutionizing veterinary diagnostics and surgery. By enhancing diagnostic accuracy, enabling precise surgical planning, and supporting personalized treatment strategies, AI can significantly improve animal health outcomes. However, addressing challenges related to data privacy, algorithm bias, and integration into clinical workflows is crucial for the widespread adoption and success of these transformative technologies.

Ethical Considerations in the Use of Artificial Intelligence in Pain Medicine

Although the integration of artificial intelligence (AI) into medicine and healthcare holds transformative potential, significant challenges must be necessarily addressed. This technological innovation requires a commitment to ethical principles. Key issues concern autonomy, reliability, and bias. Furthermore, AI development must guarantee rigorous data privacy and security standards. Effective AI implementation demands thorough validation, transparency, and the involvement of multidisciplinary teams to oversee ethical considerations. These issues also concern pain medicine where careful assessment of subjective experiences and individualized care are crucial. Notably, in this rapidly evolving technological landscape, politics plays a pivotal role in establishing rules and regulations. Regulatory frameworks, such as the European Union's Artificial Intelligence Act and recent U.S. executive orders, provide essential guidelines for the responsible use of AI. This step is crucial for balancing innovation with rigorous ethical standards, ultimately leveraging the incredible AI's benefits. As the field evolves rapidly and concepts like algorethics and data ethics become more widespread, the scientific community is increasingly recognizing the need for specialists in this area, such as AI Ethics Specialists.

Comparison of AI applications and anesthesiologist's anesthesia method choices

BACKGROUND

In medicine, Artificial intelligence has begun to be utilized in nearly every domain, from medical devices to the interpretation of imaging studies. There is still a need for more experience and more studies related to the comprehensive use of AI in medicine. The aim of the present study is to evaluate the ability of AI to make decisions regarding anesthesia methods and to compare the most popular AI programs from this perspective.

METHODS

The study included orthopedic patients over 18 years of age scheduled for limb surgery within a 1-month period. Patients classified as ASA I-III who were evaluated in the anesthesia clinic during the preoperative period were included in the study. The anesthesia method preferred by the anesthesiologist during the operation and the patient's demographic data, comorbidities, medications, and surgical history were recorded. The obtained patient data were discussed as if presenting a patient scenario using the free versions of the ChatGPT, Copilot, and Gemini applications by a different anesthesiologist who did not perform the operation.

RESULTS

Over the course of 1 month, a total of 72 patients were enrolled in the study. It was observed that both the anesthesia specialists and the Gemini application chose spinal anesthesia for the same patient in 68.5% of cases. This rate was higher compared to the other AI applications. For patients taking medication, it was observed that the Gemini application presented choices that were highly compatible (85.7%) with the anesthesiologists' preferences.

CONCLUSION

AI cannot fully master the guidelines and exceptional and specific cases that arrive in the course of medical treatment. Thus, we believe that AI can serve as a valuable assistant rather than replacing doctors.

Machine learning: implications and applications for ambulatory anesthesia

PURPOSE OF REVIEW

This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.

RECENT FINDINGS

Machine learning models have demonstrated the ability to accurately forecast case durations, Post-Anesthesia Care Unit (PACU) lengths of stay, and risk of hospital transfers based on preoperative patient and procedural factors. These models can inform case scheduling, resource allocation, and preoperative evaluation. Additionally, machine learning can standardize assessments, predict outcomes, improve handoff communication, and enrich patient education.

SUMMARY

Machine learning has the potential to revolutionize ambulatory anesthesia practice by optimizing efficiency, personalizing care, and improving quality and safety. However, limitations such as algorithmic opacity, data biases, reproducibility issues, and adoption barriers must be addressed through transparent, participatory design principles and ongoing validation to ensure responsible innovation and incremental adoption.

Current evidence on artificial intelligence in regional anesthesia

The recent advancement in regional anesthesia (RA) has been largely attributed to ultrasound technology. However, the safety and efficiency of ultrasound-guided nerve blocks depend upon the skill and experience of the performer. Even with adequate training, experience, and knowledge, human-related limitations such as fatigue, failure to recognize the correct anatomical structure, and unintentional needle or probe movement can hinder the overall effectiveness of RA. The amalgamation of artificial intelligence (AI) to RA practice has promised to override these human limitations. Machine learning, an integral part of AI can improve its performance through continuous learning and experience, like the human brain. It enables computers to recognize images and patterns specifically useful in anatomic structure identification during the performance of RA. AI can provide real-time guidance to clinicians by highlighting important anatomical structures on ultrasound images, and it can also assist in needle tracking and accurate deposition of local anesthetics. The future of RA with AI integration appears promising, yet obstacles such as device malfunction, data privacy, regulatory barriers, and cost concerns can deter its clinical implementation. The current mini review deliberates the current application, future direction, and barrier to the application of AI in RA practice.

Patient Safety in Anesthesiology: Progress, Challenges, and Prospects

Anesthesiology is considered a complex medical specialty. Its history has been marked by radical advances and profound transformations, owing to technical and pharmacological developments and innovations in the field, enabling us over the years to improve patient outcomes and perform longer, more complex surgical procedures on more fragile patients. However, anesthesiology has never been safe and free of challenges. Despite the advances made, it still faces risks associated with the practice of anesthesia, for both patients and healthcare professionals, and with some of the specific challenges encountered in low and middle-income countries. In this context, certain actions and initiatives must be carried out collaboratively. In addition, recent technologies and innovations such as simulation, genomics, artificial intelligence, and robotics hold promise for further improving patient safety in anesthesiology and overcoming existing challenges, making it possible to offer safer, more effective, and personalized anesthesia. However, this requires rigorous monitoring of ethical aspects and the reliability of the studies to reap the full benefits of the new technology. This literature review presents the evolution of anesthesiology over time, its current challenges, and its promising future. It underlines the importance of the new technologies and the need to pursue efforts and strengthen research in anesthesiology to overcome the persistent challenges and benefit from the advantages of the latest technology to guarantee safe, high-quality anesthesia with universal access.

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

PURPOSE OF REVIEW

To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns.

RECENT FINDINGS

Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies.

SUMMARY

Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.

Patient-centered precision care in anaesthesia - the PC-square (PC) 2 approach

PURPOSE OF REVIEW

This review navigates the landscape of precision anaesthesia, emphasising tailored and individualized approaches to anaesthetic administration. The aim is to elucidate precision medicine principles, applications, and potential advancements in anaesthesia. The review focuses on the current state, challenges, and transformative opportunities in precision anaesthesia.

RECENT FINDINGS

The review explores evidence supporting precision anaesthesia, drawing insights from neuroscientific fields. It probes the correlation between high-dose intraoperative opioids and increased postoperative consumption, highlighting how precision anaesthesia, especially through initiatives like Safe Brain Initiative (SBI), could address these issues. The SBI represents multidisciplinary collaboration in perioperative care. SBI fosters effective communication among surgical teams, anaesthesiologists, and other medical professionals.

SUMMARY

Precision anaesthesia tailors care to individual patients, incorporating genomic insights, personalised drug regimens, and advanced monitoring techniques. From EEG to cerebral/somatic oximetry, these methods enhance precision. Standardised reporting, patient-reported outcomes, and continuous quality improvement, alongside initiatives like SBI, contribute to improved patient outcomes. Precision anaesthesia, underpinned by collaborative programs, emerges as a promising avenue for enhancing perioperative care.

The evolution of anesthesiology education: Embracing new technologies and teaching approaches

Background and aims

Medical education requires regular reforms to include emerging best practices and technologies, while also critically evaluating effectiveness of traditional didactic teaching methods. This manuscript examines the challenges and opportunities associated with modernizing the anesthesiology curriculum.

Methods

Narrative review of literature on innovations in medical education, with a specific emphasis on anesthesiology training.

Results

Educators face difficulties implementing new teaching approaches and evaluating their effectiveness. However, active learning methods, blended with selected traditional techniques, can enhance learner engagement and competencies. Self-directed learning and simulations prepare students for real-world practice, while flipped classrooms and online platforms increase accessibility.

Conclusions

A blended approach, integrating interactive technology alongside modified lectures and seminars, may optimize anesthesiology education. Despite the promise of improved pedagogies, further research is required to assess outcomes. By embracing innovation while retaining certain foundational methods, programs can equip anesthesiologists with modern skills. This evolution is key to meeting the needs of 21st-century anesthesia care needs. Remaining at the forefront of this transformation will be vital in preparing competent future anesthesiologists through state-of-the-art education.

Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia

This review explores the intersection of artificial intelligence (AI) and anesthesia, examining its transformative impact on patient care across various phases. Beginning with a historical overview of anesthesia, we highlight the critical role of technological advancements in ensuring optimal patient outcomes. The emergence of AI in healthcare sets the stage for a comprehensive analysis of its applications in anesthesia. In the preoperative phase, AI facilitates personalized risk assessments and decision support, optimizing anesthesia planning and drug dosage predictions. Moving to the intraoperative phase, we delve into AI's role in monitoring and control through sophisticated anesthesia monitoring and closed-loop systems. Additionally, we discuss the integration of robotics and AI-guided procedures, revolutionizing surgical assistance. Transitioning to the postoperative phase, we explore AI-driven postoperative monitoring, predictive analysis for complications, and the integration of AI into rehabilitation programs and long-term follow-up. These new applications redefine patient recovery, emphasizing personalized care and proactive interventions. However, the integration of AI in anesthesia poses challenges and ethical considerations. Data security, interpretability, and bias in AI algorithms demand scrutiny. Moreover, the evolving patient-doctor relationship in an AI-driven care landscape requires a delicate balance between efficiency and human touch. Looking forward, we discuss the future directions of AI in anesthesia, anticipating advances in technology and AI algorithms. The integration of AI into routine clinical practice and its potential impact on anesthesia education and training are explored, emphasizing the need for collaboration, education, and ethical guidelines. This review provides a comprehensive overview of AI applications in anesthesia, offering insights into the present landscape, challenges, and future directions. The synthesis of historical perspectives, current applications, and future possibilities underscores the transformative potential of AI in revolutionizing patient care within the dynamic field of anesthesia.

The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments

The integration of artificial intelligence (AI) and robotics in regional anesthesia has brought about transformative changes in acute pain management for surgical procedures. This review explores the evolving landscape of AI and robotics applications in regional anesthesia, outlining their potential benefits, challenges, and ethical considerations. AI-driven pain assessment, real-time guidance for needle placement during nerve blocks, and predictive modeling solutions for nerve blocks have the potential to enhance procedural precision and improve patient outcomes. Robotic technology aids in accurate needle insertion, reducing complications and improving pain relief. This review also highlights the ethical and safety considerations surrounding AI implementation, emphasizing data security and professional training. While challenges such as costs and regulatory hurdles exist, ongoing research and clinical trials demonstrate the practical utility of these technologies. In conclusion, AI and robotics have the potential to reshape regional anesthesia practice, ultimately improving patient care and procedural accuracy in pain management.

Emerging Robotic Innovations and Artificial Intelligence in Endotracheal Intubation and Airway Management: Current State of the Art

Robotic sciences have rapidly advanced and revolutionized various aspects of medicine, including the field of airway management. Robotic endotracheal intubation is an innovative method that utilizes robotic systems to aid in the accurate placement of an endotracheal tube within the trachea. This cutting-edge technique shows great promise in improving procedural precision and ensuring patient safety. In this comprehensive overview, we delve into the present status of robotic-assisted endotracheal intubation, examining its advantages, obstacles, and the potential implications it holds for the future. In addition, this review encompasses a comprehensive analysis of the existing literature and references on recent advances in robotic technology and artificial intelligence related to airway management.