Haania Kakwan, Justin F Rousseau, Lidia M Moura, Irfan S Sheikh
{"title":"Ambient technology in epilepsy clinical practice.","authors":"Haania Kakwan, Justin F Rousseau, Lidia M Moura, Irfan S Sheikh","doi":"10.1002/epi4.70066","DOIUrl":null,"url":null,"abstract":"<p><p>The utilization of large language model-based artificial intelligence (AI) in the field of neurology has gained attention as a viable tool to enhance and assist providers with processes ranging from scheduling patients to providing preliminary interpretations of testing results, pending orders, and documenting encounters. Epileptologists could benefit from these technologies by utilizing ambient AI models, recent applications of which offer promising solutions for automating clinical documentation. While the potential benefits of using these tools are significant and include reduced physician burnout and improved patient experience, the deployment of these technologies also raises critical concerns, such as potential biases in model training and the risk of errors being inserted into the electronic health record (EHR), among other yet to be realized unintended consequences. The accuracy of clinical documentation is essential in epilepsy care, where detailed seizure histories and accurate medication records are critical to patient safety. Another concern may be paradoxically increased physician burnout as increased expectations of providers are created. This article examines the challenges, risks, and practical considerations in applying documentation tools that utilize ambient intelligence (AmI) for outpatient epilepsy clinic encounters, highlighting key examples from clinical practice and underscoring the importance of human oversight. Although AmI models may enhance clinical documentation efficiency as measured by time to close a note and reduced rates of burnout in providers, their role in clinical environments must be carefully regulated, with further studies needed to validate this claim, provide ongoing monitoring of performance, and establish safeguards for patient safety. Collaborative efforts among clinicians, clinical informatics professionals, AI developers, and regulatory bodies are pressingly needed to ensure the safe deployment of AmI into clinical care settings. PLAIN LANGUAGE SUMMARY: Ambient artificial intelligence technology takes advantage of sensors embedded in the environment to automate tasks without the need for human input. It has the potential to streamline numerous tasks within outpatient epilepsy clinics and reduce the workload of providers as well as improve patient care. This technology has already been brought to the market as a tool for clinical documentation. The current challenges and limitations associated with its implementation require careful human oversight, which we show with examples. Further research, regulations, and ongoing monitoring are necessary to ensure ambient artificial intelligence benefits both patients and healthcare providers while minimizing risks.</p>","PeriodicalId":12038,"journal":{"name":"Epilepsia Open","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epilepsia Open","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/epi4.70066","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The utilization of large language model-based artificial intelligence (AI) in the field of neurology has gained attention as a viable tool to enhance and assist providers with processes ranging from scheduling patients to providing preliminary interpretations of testing results, pending orders, and documenting encounters. Epileptologists could benefit from these technologies by utilizing ambient AI models, recent applications of which offer promising solutions for automating clinical documentation. While the potential benefits of using these tools are significant and include reduced physician burnout and improved patient experience, the deployment of these technologies also raises critical concerns, such as potential biases in model training and the risk of errors being inserted into the electronic health record (EHR), among other yet to be realized unintended consequences. The accuracy of clinical documentation is essential in epilepsy care, where detailed seizure histories and accurate medication records are critical to patient safety. Another concern may be paradoxically increased physician burnout as increased expectations of providers are created. This article examines the challenges, risks, and practical considerations in applying documentation tools that utilize ambient intelligence (AmI) for outpatient epilepsy clinic encounters, highlighting key examples from clinical practice and underscoring the importance of human oversight. Although AmI models may enhance clinical documentation efficiency as measured by time to close a note and reduced rates of burnout in providers, their role in clinical environments must be carefully regulated, with further studies needed to validate this claim, provide ongoing monitoring of performance, and establish safeguards for patient safety. Collaborative efforts among clinicians, clinical informatics professionals, AI developers, and regulatory bodies are pressingly needed to ensure the safe deployment of AmI into clinical care settings. PLAIN LANGUAGE SUMMARY: Ambient artificial intelligence technology takes advantage of sensors embedded in the environment to automate tasks without the need for human input. It has the potential to streamline numerous tasks within outpatient epilepsy clinics and reduce the workload of providers as well as improve patient care. This technology has already been brought to the market as a tool for clinical documentation. The current challenges and limitations associated with its implementation require careful human oversight, which we show with examples. Further research, regulations, and ongoing monitoring are necessary to ensure ambient artificial intelligence benefits both patients and healthcare providers while minimizing risks.