Nature Machine Intelligence | 2021
Harnessing the power of artificial intelligence to transform hearing healthcare and research
Abstract
1Ear Institute, University College London, London, UK. 2Royal National ENT Hospital & University College London Hospitals, London, UK. 3NIHR UCLH BRC Deafness and Hearing Problems Theme, Ear Institute, University College London, London, UK. 4Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. 5Curai Health, Seattle, WA, USA. 6Departments of Surgery, Biomedical Engineering, and Electrical and Computer Engineering, Duke University, Durham, NC, USA. 7Center for Hearing Research, Departments of Anatomy and Neurobiology, Biomedical Engineering, Cognitive Sciences, Otolaryngology–Head and Neck Surgery, University of California Irvine, Irvine, CA, USA. ✉e-mail: [email protected]; [email protected] Hearing was once at the forefront of technological innovation. The cochlear implant, which restores hearing through direct electrical stimulation of the auditory nerve, was a revolutionary advance and remains the most successful neural prosthetic in terms of both performance and penetration1,2. Even hearing aids, now considered staid, once led the way in the miniaturization of digital electronics3. But innovation has stalled, and hearing healthcare is struggling to meet a growing global burden; the vast majority of those with hearing loss do not receive treatment, and those who do often receive only limited benefit. Recent advances in artificial intelligence (AI) have the potential to transform hearing. Machines have already achieved human-like performance in important hearing-related tasks such as automatic speech recognition (ASR)4,5 and natural language processing6,7. AI is also starting to have an impact in medicine; for example, eye screening technologies based on deep neural networks (DNNs) are already in worldwide use. But there are few applications related to hearing per se, and AI remains absent from hearing healthcare. In this Perspective, we describe opportunities to use existing technologies to create clinical applications with widespread impact, as well as the potential for new technologies that faithfully model the auditory system to enable fundamental advances in hearing research. The disconnect between AI and hearing has deep roots. In contrast to modern machine vision, which began with the explicit goal of mimicking the visual cortex8 and continues to draw inspiration from the visual system9, work in modern machine hearing has never prioritized biological links. The earliest attempts at ASR were, in fact, modelled on human speech processing, but this approach was largely unsuccessful. The first viable ASR systems arose only after the field made a deliberate turn away from biology (with rationale neatly summarized by IBM’s Frederick Jelinek: “Airplanes don’t flap their wings”10) to focus on modelling the statistical structure of the temporal sequences in speech and language via hidden Markov models. The recent incorporation of DNNs into machine hearing systems has further improved their performance in specific tasks, but it has not brought machine hearing any closer to the auditory system in a mechanistic sense. Biological replication is not necessarily a requirement: many of the important clinical challenges in hearing can be addressed using models with no relation to the auditory system11 (for example, DNNs for image classification) or models that mimic only certain aspects of its function12,13 (such as DNNs for sound source separation). But for the full potential of AI in hearing to be realized, new machine hearing systems that match both the function of the auditory system and key elements of its structure are needed. We envision a future in which the natural links between machine hearing and biological hearing are leveraged to provide effective hearing healthcare across the world and enable progress in hearing’s most complex research challenges. To motivate this future, we first provide a brief overview of the auditory system and its disorders and describe the potential of AI to address urgent and important needs in hearing healthcare. We then outline the steps that must be taken to bridge the present disconnect between AI and hearing and suggest directions for future work to unite the two fields in working towards the development of true artificial auditory systems.