A Resident’s View of AI in Otolaryngology

Yet, AI is already showing up in useful, focused ways. AI scribes built into the electronic health record (EHR) can produce notes faster and make note-taking feel less burdensome for clinicians, with early studies reporting time savings and better workflow (JAMA Network Open. doi: 10.1001/jamanetworkopen.2024.60637). Dr. Gantwerker said about EHR integration, “The biggest near-term win is facilitating the patient encounter. Ambient listening technology and point-of-care decision support tools may make our job easier. We all experience burnout from spending too much time charting and information overload from working in the EHR, and I think AI is the solution.”

That perspective is echoed by Murugappan Ramanathan, MD, professor of otolaryngology–head and neck surgery and vice director of clinical operations at Johns Hopkins Health Care and Surgery Center in Bethesda, Md., “The integration of AI scribes into clinic has resulted in more meaningful patient interactions and reduced the documentation burden, much of which occurs after hours, especially in our department. Preliminarily, we have seen that our department faculty have also been able to increase the number of patients seen in a clinic session, as well, which has improved patient access without adding more sessions.”

Inbox help is emerging, too. AI-drafted replies can cut the effort needed to answer patient messages, if teams keep quality checks in place (JAMA Network Open. doi: 10.1001/jamanetworkopen.2024.3201). In head and neck cancer, models that read imaging and pathology can help flag characteristics like extranodal extension; some studies show performance on par with human readers, but most of this work still needs real-world testing before routine use (Laryngoscope.doi.org/10.1002/lary.70194). In laryngology, reviews find promising accuracy for voice-disorder tools, while noting the lack of prospective clinical trials so far (Otolaryngologists Head Neck Surg. doi.org/10.1002/ohn.636). In rhinology, automated CT scoring is moving toward multi-site validation to help standardize how we describe radiologic disease. (Int Forum Allergy Rhinol. doi.org/10.1002/alr.23410).

These tools can ease routine work and improve consistency, even when physician input is required to validate their accuracy. In the surgical training realm, Dr. Gantwerker supports using AI to augment study time and synthesis, using “AI as virtual tutors and for summarizing and synthesizing large amounts of data. Leveraging tools like Google’s NotebookLM can help create study guides, learning podcasts, and multiple-choice questions, and can pull specific data from reference PDFs,” he said. These varied applications have the potential to drastically improve efficiency, even if they do not replace necessary human judgment.