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Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

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Hyperscanning is a technique which simultaneously records the neural activity of two or more people. This is done using one of several neuroimaging methods, such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The use of hyperscanning has seen a dramatic rise in recent years to monitor social interactions between two or more people. Similarly, there has been an increase in the use of virtual reality (VR) for collaboration, and an increase in the frequency of social interactions being carried out in virtual environments (VE). In light of this, it is important to understand how interactions function within VEs, and how they can be enhanced to improve their quality in a VE. In this paper, we present some of the work that has been undertaken in the field of social neuroscience, with a special emphasis on hyperscanning. We also cover the literature detailing the work that has been carried out in the human–computer interaction domain that addresses remote collaboration. Finally, we present a way forward where these two research domains can be combined to explore how monitoring the neural activity of a group of participants in VE could enhance collaboration among them.