The practice of anesthesia requires clinicians to interpret subtle patient responses while managing complex pharmacology. Traditionally, anesthesiologists relied on physiologic markers such as heart rate, blood pressure, and clinical observation to judge the depth of sedation. While effective, these indicators are not perfect, and there is a risk of under- or over-sedation. Advances in artificial intelligence (AI) and electroencephalography (EEG) are now transforming this process, improving anesthesiologists’ ability to tailor sedation in real time.
EEG provides a direct measurement of brain activity by recording electrical signals at the scalp. During anesthesia, characteristic EEG patterns emerge as the brain transitions through stages of sedation and unconsciousness. This makes it is a valuable tool for determining whether a patient is adequately sedated, especially in settings where precise control is crucial, such as neurosurgery or in elderly patients vulnerable to postoperative cognitive dysfunction 1–4.
While EEG is rich in information, its complexity means that not all the information it provides can be interpreted quickly by physicians. Machine learning algorithms, meanwhile, can analyze continuous EEG streams and detect patterns associated with awareness or excessive suppression. These models are trained on large datasets of EEG recordings paired with anesthetic drug levels and patient outcomes, potentially allowing them to recognize subtle features that even expert clinicians might miss. AI-driven systems may also be able to predict how a patient will respond to incremental drug doses based on EEG data. This predictive capability would help anesthesiologists avoid over-sedation, reducing the risk of hypotension, delayed emergence, or postoperative delirium 5–7.
Every patient responds differently to anesthetic agents due to variations in age, genetics, comorbidities, and concurrent medications. Traditional dosing strategies often rely on weight-based formulas and population averages, which may not capture these individual differences. A central goal of applying AI to EEG data during anesthesia is to be able to adjust dosing recommendations dynamically. For example, a lower dose may be sufficient in an older adult whose brain exhibits stronger sensitivity to anesthetics, while a younger patient might require more. This personalization can lead to faster recovery times, reduced drug consumption, and fewer complications 8–10.
The clinical benefits of AI-guided EEG analysis for anesthesia and sedation are driving further research and development. Some data show that patients monitored with AI-enhanced systems experience fewer episodes of intraoperative awareness and are less likely to suffer from postoperative cognitive decline. Operating room efficiency improves as emergence from anesthesia becomes more predictable. In addition, precise dosing can reduce the cumulative exposure to anesthetic drugs, lowering the risk of organ toxicity and promoting faster discharge from recovery units.
Challenges remain, though. Integration into clinical workflows requires robust validation, user-friendly interfaces, and training for anesthesiologists. There are also concerns about overreliance on automated systems—AI should complement, not replace, clinical judgment. Finally, questions around data privacy and the need for large, diverse datasets to ensure generalizability must be addressed 9–16.
As technology matures and adoption spreads, AI-enhanced EEG monitoring may become the standard of care, redefining how anesthesia is delivered to each individual patient.
References
1. He, B., Sohrabpour, A., Brown, E. & Liu, Z. Electrophysiological Source Imaging: a Noninvasive Window to Brain Dynamics. Annu Rev Biomed Eng 20, 171–196 (2018). DOI: 10.1146/annurev-bioeng-062117-120853
2. Nunez, P. Chapter 5 EEG: A Window on the Mind. in Brain, Mind, and the Structure of Reality (ed. Nunez, P.) 0 (Oxford University Press, 2010). DOI:10.1093/acprof:oso/9780195340716.003.0005.
3. Constant, I. & Sabourdin, N. The EEG signal: a window on the cortical brain activity. Pediatric Anesthesia 22, 539–552 (2012). DOI: 10.1111/j.1460-9592.2012.03883.x
4. Molinas, M. A Window into the Brain: Bringing EEG Neuroimaging into Home-Based BCIs: “Advancing EEG Neuroimaging for Neurorehabilitation, Communication, and Dream Decoding”. (2025). DOI:10.13140/RG.2.2.24803.46883.
5. Schmierer, T., Li, T. & Li, Y. Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment. Artificial Intelligence in Medicine 151, 102869 (2024). DOI: 10.1016/j.artmed.2024.102869
6. Tveit, J. et al. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol 80, 805–812 (2023). DOI: 10.1001/jamaneurol.2023.1645
7. Tung, C.-S., Liang, S.-F., Chang, S.-F. & Young, C.-P. A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation. IEEE Journal of Biomedical and Health Informatics 29, 2629–2641 (2025). DOI: 10.1109/JBHI.2024.3496996
8. Han, L. et al. Deep learning models using intracranial and scalp EEG for predicting sedation level during emergence from anaesthesia. BJA Open 12, (2024). DOI: 10.1016/j.bjao.2024.100347
9. Cao, Y., Wang, Y., Liu, H. & Wu, L. Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology. Front Med (Lausanne) 12, 1571725 (2025). DOI: 10.3389/fmed.2025.1571725
10. Sheikh, S. S. B. V., Samatha Ampeti, Mansi Srivastava, Shubham Ravindra Sali, Patel Nirali Kirankumar, Raziya Begum. Artificial Intelligence Driven Innovation in Anesthesia for Personalized Perioperative Care. medtigo Journal of Anesthesiology and Pain Medicine 1, (2025). DOI: 10.63096/medtigo3067125
11. Song, B., Zhou, M. & Zhu, J. Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security. Med Sci Monit 29, e938835-1-e938835-15 (2023). DOI: 10.12659/MSM.938835
12. Liu, K., Qiu, W. & Yang, X. Exploring the growth and impact of artificial intelligence in anesthesiology: a bibliometric study from 2004 to 2024. Front. Med. 12, (2025). DOI: 10.3389/fmed.2025.1595060
13. Meijden, S. L. van der, Arbous, M. S. & Geerts, B. F. Possibilities and challenges for artificial intelligence and machine learning in perioperative care. BJA Education 23, 288–294 (2023). DOI: 10.1016/j.bjae.2023.04.003
14. Murdoch, B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics 22, 122 (2021). DOI: 10.1186/s12910-021-00687-3
15. Cordero, D. The downsides of artificial intelligence in healthcare. Korean J Pain 37, 87–88 (2024). DOI: 10.3344/kjp.23312
16. khan, B. et al. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed Mater Devices 1–8 (2023) DOI:10.1007/s44174-023-00063-2.