„Machine learning“ in der Anästhesiologie

Sassenscheidt, Julian; Jungwirth, Bettina; Kubitz, Jens C.

doi:10.1007/s00101-020-00764-z

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Finally, the prerequisite for ML algorithms is to collect large amounts of high-fidelity physiological monitoring data from patients. If data are incomplete, unstable, biased, or incorrect in the training process, it may produce false results and lead doctors to make the bad decisions [ 88 ]. Therefore, the opacity and irrationality of the ML model operation mode will significantly limit its application.…”

Section: Challenges Of Ai In Anesthesiology Developmentmentioning

confidence: 99%

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

2023

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The rapid development of artificial intelligence (AI) technology is due to the significant progress in big data, databases, algorithms, and computing power, and medical research is a vital application direction of AI. The integrated development of AI and medicine has improved medical technology, and the efficiency of medical services and equipment has enabled doctors to better serve patients. The tasks and characteristics of the anesthesia discipline also make AI necessary for its development, and AI has also been initially applied in different fields of anesthesia. Our review aims to clarify the current situation and challenges of AI application in anesthesiology to provide clinical references and guide the future development of AI in anesthesiology. This review summarizes progress in the application of AI in perioperative risk assessment and prediction, deep monitoring and regulation of anesthesia, essential anesthesia skills operation, automatic drug administration systems, and teaching and training in anesthesia. Also discussed herein are the accompanying risks and challenges of applying AI in anesthesia: patient privacy and information security, data sources, and ethical issues, lack of capital and talent, and the “black box” phenomenon.

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Section: Challenges Of Ai In Anesthesiology Developmentmentioning

confidence: 99%

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

2023

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“…The model is trained with example data, automatically adapts to increase its predictive performance, and finally, is evaluated by applying it to data not included in the training process [15]. These models may help clinicians with additional information on diagnosis, treatment, risk, prognosis, and much more for a currently treated patient with several possible applications in anesthesiology [16,17]. Although this offers exceptional opportunities to improve medical care in the future, there are still obstacles to overcome before a broad application is feasible.…”

Section: Introductionmentioning

confidence: 99%

Big Data in Studying Acute Pain and Regional Anesthesia

Müller-Wirtz

Volk

2021

JCM

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The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyses have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials. As a consequence, the utilization of big data in acute pain and regional anesthesia research has considerably increased over the last decade. Multicentric clinical registries and administrative databases (e.g., healthcare claims databases) have collected millions of cases until today, on which basis several important research questions were approached. In acute pain research, big data was used to assess postoperative pain outcomes, opioid utilization, and the efficiency of multimodal pain management strategies. In regional anesthesia, adverse events and potential benefits of regional anesthesia on postoperative morbidity and mortality were evaluated. This article provides a narrative review on the growing importance of big data for research in acute postoperative pain and regional anesthesia.

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„Machine learning“ in der Anästhesiologie

Cited by 2 publications

References 34 publications

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

Big Data in Studying Acute Pain and Regional Anesthesia

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