In the rapidly evolving landscape of healthcare IT, AI has emerged as a pivotal force to help health professionals become more efficient and effective. The adoption of AI in the medical field has varied by specialty and application, with some areas moving faster than others. For many years, cardiology, for example, has used AI for ECG and imaging tests to detect subtle cardiac abnormalities more easily and to deliver timelier evaluations. The same is true in radiology and mammography, where the use of AI technology has surged in the last decade.1
AI is also making significant headway within neurology, where new tools that apply deep learning for interpreting and analyzing EEG data² hold great promise. Many experts believe that the integration of neuro AI technology will have greater impact than many other use cases of AI in the medical field. This has to do with many factors regarding EEG interpretation and analysis, such as:
However, there are some obstacles to rapid adoption of neuro AI tools. Distrust of AI technology is a huge factor that must be overcome. More protocols for the integration of AI, along with training for neurologists, epileptologists, neurodiagnostic personnel, and other professionals, will build greater confidence in the technology.
Research also demonstrates that overcoming the perception that AI will replace expertise is key³ to the broad adoption of AI for EEG interpretation. Neurology teams must view AI tools as helpful and reliable assistants that save time, increase accuracy, and improve patient care, rather than a replacement for human expertise. When that mind shift occurs, neuro care teams can take full advantage of human-machine teaming, where AI frees up physicians and others to perform the most critical tasks in diagnosing and treating brain abnormalities and conditions.
Once these barriers to adoption have been overcome, integrating AI as a supportive technology for EEG interpretation can have remarkably positive impacts on efficiency, cost, and, eventually, patient outcomes. While AI has numerous practical applications for EEG, this article summarizes five of the most compelling reasons AI holds immense promise for EEG analysis.
Traditional EEG interpretation relies heavily on human expertise for tedious and repetitive tasks. Even the most experienced clinicians can misinterpret subtle patterns or overlook critical details within dense EEG data, leading to inconsistent results or even misdiagnosis. Machine learning algorithms can be trained on vast datasets containing diverse EEG patterns, enabling more rapid detection of anomalies, patterns, and abnormalities. Even more advanced AI tools that use deep learning algorithms can simultaneously process and analyze intricate temporal and spatial relationships within EEG data.
Traditional methods of EEG interpretation are often subjective and qualitative, with no universal standard in wide use by EEG practices today.4 This problem is exacerbated by the fact that EEG expertise is not always readily available, and even experienced specialists may not have fellowship training. The ability to train AI algorithms on extremely large datasets involving a wide range of EEG patterns enables standardization across diverse specialties and patient populations. While traditional algorithms require manual effort to refine their use for a specific dataset or group, AI algorithms can process vast datasets objectively and consistently. This helps encourage uniformity in EEG reporting and establishes benchmarks for evaluating each patient’s condition over time.
A multidisciplinary approach is more effective in today’s healthcare landscape. The treatment of neurology patients often requires input from large teams of neurologists, epileptologists, neurodiagnostic professionals, and other specialists. AI-assisted computation can swiftly sort and examine data, and produce an array of interpretations, evaluations of risks, probabilities of treatments, and statistical data derived from the patient’s medical history in conjunction with datasets of existing, validated EEG reports. In fact, research reports that human teaming with intelligent machines has become a fundamental characteristic of a successful clinical decision support system.5 Even with advanced AI technology, qualitative human perspectives remain crucial to the success of complex decision support systems.
The integration of AI in EEG interpretation ushers in a new era of efficiency and speed. The massive volume of data generated by EEG recordings can overwhelm manual interpretation. AI algorithms can swiftly sift through this data, highlighting segments of potential abnormality. This targeted approach reduces the burden on human reviewers and helps ensure that no critical information goes unnoticed. Moreover, AI can process prolonged recordings that might be taxing for human reviewers, thereby improving the overall quality of EEG analysis. AI’s rapid processing capabilities can expedite EEG analysis, allowing clinicians to focus their valuable time on reviewing critical cases and devising treatment plans. Routine cases can be handled efficiently by AI algorithms, giving neurologists and other clinicians more time to concentrate on complex cases. This is particularly helpful in remote and underserved areas where EEG expertise is scarce or simply unavailable.
The integration of AI in EEG interpretation ushers in a new era of efficiency and speed. The massive volume of data generated by EEG recordings can overwhelm manual interpretation. AI algorithms can swiftly sift through this data, highlighting segments of potential abnormality. This targeted approach reduces the burden on human reviewers and helps ensure that no critical information goes unnoticed. Moreover, AI can process prolonged recordings that might be taxing for human reviewers, thereby improving the overall quality of EEG analysis. AI’s rapid processing capabilities can expedite EEG analysis, allowing clinicians to focus their valuable time on reviewing critical cases and devising treatment plans. Routine cases can be handled efficiently by AI algorithms, giving neurologists and other clinicians more time to concentrate on complex cases. This is particularly helpful in remote and underserved areas 6 where EEG expertise is scarce or simply unavailable.
Across the board, AI adoption in healthcare over the next five years has been predicted to reduce costs by as much as $360 billion.7 Experts attribute a large portion of these savings to increased labor productivity, especially as human/machine teaming becomes more commonplace. Incorporating AI technology in a thoughtful manner can optimize the work of skilled neurologists, epileptologists, and other clinicians by reducing the resources spent on repetitive, costly, and time-consuming EEG interpretation tasks.
As we look ahead to the future of healthcare, AI’s transformative potential in EEG interpretation and analysis is undeniable. The ability of neuro AI to enhance accuracy, improve standardization interpretation, support collaborative decision-making, boost profitability, and expedite processes is reshaping how we approach patient care. The synergy between human expertise and AI’s computational prowess promises to unlock new insights into brain activity, leading to more accurate interpretation, earlier interventions, and improved patient outcomes.
1. “ Lee LIT, Kanthasamy S, Ayyalaraju RS, Ganatra R. The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine. BJR Open. 2019 Oct 16;1(1):20190037. doi: 10.1259/bjro.20190037. PMID: 33178956; PMCID: PMC7592467.
2. Tveit J, Aurlien H, Plis S, et al. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol. 2023;80(8):805–812. doi:10.1001/jamaneurol.2023.1645
3. Henry, K.E., Kornfield, R., Sridharan, A. et al. Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system. npj Digit. Med. 5, 97 (2022). https://doi.org/10.1038/s41746-022-00597-7
4. Grant AC, Abdel-Baki SG, Weedon J, Arnedo V, Chari G, Koziorynska E, Lushbough C, Maus D, McSween T, Mortati KA, Reznikov A, Omurtag A. EEG interpretation reliability and interpreter confidence: a large single-center study. Epilepsy Behav. 2014 Mar;32:102-7. doi: 10.1016/j.yebeh.2014.01.011. Epub 2014 Feb 13. PMID: 24531133; PMCID: PMC3965251.
5.Russell S, Kumar A. Providing Care: Intrinsic Human-Machine Teams and Data. Entropy (Basel). 2022 Sep 27;24(10):1369. doi: 10.3390/e24101369. PMID: 37420389; PMCID: PMC9601264.
6. Tveit J, Aurlien H, Plis S, et al. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol. 2023;80(8):805–812. doi:10.1001/jamaneurol.2023.1645
7. Sahni, N R, G Stein, R Zemmel, and D M Cutler (2023), “The potential impact of artificial intelligence on healthcare spending”, in A Agrawal, J Gans, A Goldfarb, and C Tucker (eds.), The Economics of Artificial Intelligence: Health Care Challenges.
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