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Background Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals. Objective This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review. Methods A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report’s scope and details was collected to assess the commonalities among the approaches. Results A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results. Conclusions In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.

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The global digital transformation enables computational audiology for advanced clinical applications that have the potential to impact the global burden of hearing loss. In this paper we describe emerging hearing-related artificial intelligence applications and argue for their potential to improve access, precision and efficiency of hearing health care services. In addition, we raise awareness of risks that must be addressed to enable a safe digital transformation in audiology. We envision a future where computational audiology is implemented via open-source models using interoperable shared data and where health care providers adopt new roles within a network of distributed expertise. All of this should take place in a health care system where privacy, the responsibility of each stakeholder and, most importantly, the safety and autonomy of patients are all guarded by design.

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Speech recognition software has become increasingly sophisticated and accurate due to progress in information technology. The software converts speech into text using artificial intelligence. The intended purpose of most developed apps is taking voice commands and taking notes. Nevertheless, some apps are specially developed for the hearing impaired and deaf. This project aims to examine the performance of speech recognition apps and to explore which audiological tests are a representative measure of the ability of these apps to convert speech into text.

The global carbon footprint of medicine is about 5% of global CO2 emissions. This is quite a chunk of our remaining carbon budget to stay within 1.5 degrees of global warming. For that reason, countries have pledged to decrease the carbon footprint of their healthcare systems. So what is the carbon footprint of hearing healthcare? And what strategies and initiatives exist to lower its footprint? In this perspective paper, we would like to start addressing that question.

The condition of the auditory nerve is a factor determining hearing performance of cochlear implant (CI) recipients. Abnormal loudness adaptation is associated with poor auditory nerve survival. We examined which stimulus conditions are suitable for tone decay measurements to differentiate between CI recipients with respect to their speech perception. Tone decay was defined here as occurring when the percept disappears before the stimulus stops. We measured the duration of the percept of a 60-s pulse train. Current levels ranged from below threshold up to maximum acceptable loudness, pulse rates from 250 to 5000 pulses/s, and duty cycles (percentages of time the burst of pulses is on) from 10% to 100%. Ten adult CI recipients were included: seven with good and three with poor speech perception. Largest differences among the subjects were found at 5000 pulses/s and 100% duty cycle. The well performing subjects had a continuous percept of the 60-s stimulus within 3 dB above threshold. Two poorly performing subjects showed abnormal loudness adaptation, that is, no continuous percept even at levels greater than 6 dB above threshold. We conclude that abnormal loudness adaptation can be detected via an electric tone decay test using a high pulse rate and 100% duty cycle.