Decision-making in anesthesiology: will artificial intelligence make intraoperative care safer?

Abstract

Purpose of review This article explores the impact of recent applications of artificial intelligence on clinical anesthesiologists’ decision-making. Recent findings Naturalistic decision-making, a rich research field that aims to understand how cognitive work is accomplished in complex environments, provides insight into anesthesiologists’ decision processes. Due to the complexity of clinical work and limits of human decision-making (e.g. fatigue, distraction, and cognitive biases), attention on the role of artificial intelligence to support anesthesiologists’ decision-making has grown. Artificial intelligence, a computer's ability to perform human-like cognitive functions, is increasingly used in anesthesiology. Examples include aiding in the prediction of intraoperative hypotension and postoperative complications, as well as enhancing structure localization for regional and neuraxial anesthesia through artificial intelligence integration with ultrasound. Summary To fully realize the benefits of artificial intelligence in anesthesiology, several important considerations must be addressed, including its usability and workflow integration, appropriate level of trust placed on artificial intelligence, its impact on decision-making, the potential de-skilling of practitioners, and issues of accountability. Further research is needed to enhance anesthesiologists’ clinical decision-making in collaboration with artificial intelligence.

Transformative Landscape of Anesthesia Education: Simulation, AI Integration, and Learner-Centric Reforms: A Narrative Review

“…The arrival of an information-driven society with a foundation in data science and AI is causing significant changes in the field of medicine. The innovative transformation of the medical system is inevitable, leading to continuous changes in the roles of medical systems and healthcare providers [8,9]. Anticipating changes in the ethical or professional aspects of healthcare providers' actions based on the overall perspective of society, it is essential for medical societies and educational institutions to share information and cooperate for the training of healthcare professionals, including those capable of adapting to the AI era [10].…”

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

“…37 However, synthesizing these data from the electronic health record in an automated fashion can be challenging due to data fidelity and heterogeneity, and such systems raise new regulatory concerns such as safety, liability, and accountability. [38][39][40][41] The advent of generative artificial intelligence including large language models such as ChatGPT (OpenAI, Inc., USA) 42 and those trained on clinical expertise such as Med-PaLM (Med-Pathways Language Model, Alphabet Inc., USA) 43 have the potential to greatly augment clinical practice. Large language models are trained on vast quantities of natural language to learn statistical relationships between words to mimic human understanding of language, and these language representations can then be used to construct generative models to produce novel output.…”

“…Successful oversight will require a multidisciplinary approach that includes clinicians, data scientists, information technology specialists, human factors engineers, implementation scientists, ethicists, and regulatory experts to develop artificial intelligence initiatives that meet clinical need; can be seamlessly integrated into clinical workflows; comply with regulatory requirements; and are thoroughly tested to ensure their accuracy, generalizability, and safety. 39,59 The governance body should objectively evaluate how a model was constructed, assess whether active steps were taken to mitigate inherent biases, monitor model validation and testing to avoid phenomena such as calibration or prediction drift, 60 and evaluate whether the risk-benefit landscape of a model needs to meet a standard of transparency and explainability. 61 Collaborations with other healthcare organizations and academic institutions to share knowledge and best practices related to artificial intelligence in healthcare may help develop strategies to achieve these goals.…”

“…As artificial intelligence technologies continue to advance and become more prevalent, several ethical concerns have been raised, such as considerations of equity and biases, job displacement, de-skilling, and accountability in the context of autonomous decision-making. 39,70 Artificial intelligence systems in healthcare must adhere to ethical and regulatory standards. As an example, the Food and Drug Administration has released a set of guiding principles for good machine learning practice and device development.…”