Significant obstacles remain, though. Otolaryngology doesn’t yet have standardized guidelines for an otitis media classification system, or large, labeled image datasets, said Dr. Moberly, who is also working to develop AI software to aid in ear pathology diagnosis. Additionally, clinicians and patients are leery of AI diagnosis. “Right now, there’s no real check on the accuracy of my otoscopic exams and diagnosis,” Dr. Moberly said. “I think some people are hesitant to accept something that’s going to serve as a check on their ability to diagnose.”
Explore This Issue
January 2023Patients perceive “more accurate diagnosis” as a primary strength of AI, in part due to its ability to draw upon more data than humans, according to a 2021 systematic review (Lancet Digit Health. 2021;3:e599–e611). But they also say “less accurate diagnosis” is a primary weakness of AI and express concerns about computer-based tools missing diagnoses due to a lack of context or human experience. A study published in 2022 found that 31% to 40.5% of surveyed respondents would be “somewhat” or “very” uncomfortable receiving a diagnosis from an AI algorithm that was accurate 90% of the time but incapable of explaining its rationale (JAMA Netw Open. 2022:5:e2210309).
The so-called “black box” issue—the inability to determine how or why AI algorithms arrive at conclusions—is a major limitation to the adoption of AI in clinical practice. “Many of these machine learning models don’t provide a lot of transparency. We don’t really understand how they’re making decisions internally,” Dr. Bur said. He believes that additional testing and prospective trials demonstrating safety and efficacy are necessary before AI tools are introduced into clinical practice.
Developers working on an AI support algorithm to assist with vestibular disorder diagnosis performed a small randomized controlled trial to compare diagnoses generated by primary care physicians and neuro-otology specialists. One group of primary care providers diagnosed patients with persistent dizziness via traditional methods; another also used the AI support tool. Neuro-otology experts then assessed the same patients. Researchers found that the AI-assisted primary care diagnoses agreed with the expert diagnosis in 54% of cases, compared to just 41% of cases evaluated by primary care providers without AI assistance (J Neurol. 2022;269:2584–2598).
Diagnosing Sinusitis
Scientists have been studying the potential of AI to detect and diagnose sinusitis based on radiographs since at least 2019 (Quant Imaging Med Surg. 2019;9:942–951). A 2021 study noted that an AI algorithm showed diagnostic performance comparable to radiologists in diagnosing frontal, ethmoid, and maxillary sinusitis (Diagnostics (Basel). 2021;11:250. doi:10.3390/diagnostics11020250). To date, however, no AI rhinological applications have been validated in a real-world setting, leading the authors of a 2022 systematic review to conclude that “AI applications remain more theoretical than useful in day-to-day clinics” (Eur Arch Otorhinolaryngol. October 19, 2022; doi:10.1007/s00405-022-07701-3).