Do I sound sick?
Authors: None The Lancet Digital Health
Journal: The Lancet Digital Health (2021)

Our last Editorial discussed the need for greater public voices in the development of digital health technologies. Rather ironically, in this issue of the journal, Harry Coppock and colleagues comment on how the voices of the public are instead being used to diagnose COVID-19. Vocal patterns such as pitch, tone, rhythm, and rate as well as breathing and coughing can be processed by artificial intelligence (AI) to develop vocal biomarkers of disease. AI-powered vocal biomarkers are being developed to diagnose a multitude of disorders including Parkinson's disease, cardiovascular disease, and post-traumatic stress disorder. It has also been suggested that voice technology could be used to predict body-mass index. The advent of voice technology such as Amazon Alexa, Google Assistant, and Apple's Siri has helped grow audio data and with it, the potential to advance and transform a myriad of industries including health care. But is it possible to diagnose illness from your voice? Harry Coppock, lead author of the Comment told The Lancet Digital Health that “These technologies have the potential to dramatically improve global health through rapid and inexpensive diagnosis that could be run on personal devices—the perfect tool against a pandemic.” However, Coppock and colleagues highlight that further evidence is needed to realise the full potential for a digital mass test for COVID-19. Coppock and colleagues list seven key issues with vocal biomarker research, which prevent it from being clinically useful in diagnosing COVID-19. These issues include the possibility that AI cannot discriminate between respiratory disorders; audio data is subject to many undetectable biases; there is a lack of clinically relevant ground truth; and external validation is still rare in current research due to a lack of available data. As with any diagnostic tool, prospective randomised controlled trials and real-world evaluation are necessary to evaluate the AI before scaling up its use. However, due to the relative simplicity of delivering these vocal diagnostic tools, companies might take their technology directly to the public. One such company, Sonde Health, uses audio signal processing and machine learning to detect symptoms of asthma and chronic obstructive pulmonary disease, and as an early warning system for COVID-19. In July, 2021, Sonde Health announced a new partnership with Qualcomm that could potentially bring the technology to smartphones and smart speakers, which could help Sonde Health reach a far broader audience. Though these technologies could soon be accessed directly on your mobile phone, there are many ethical and technological challenges to overcome before they are proven to be useful for health. Guy Fagherazzi, Group Leader of the Deep Digital Phenotyping Research Unit at Luxembourg Institute of Health told The Lancet Digital Health that “These are indeed very promising as non-invasive tools for screening or triage, but in reality the safety and performance of the algorithms still needs to be confirmed.” Without proper validation and evaluation, vocal biomarker diagnostic tools could engender a false sense of security in the public, or overwhelm health services. Fagherazzi says, “Vocal biomarkers and its embedding in a digital technology (smartphone app, voice assistant, smart mirrors...) should be co-designed by the end users. And this is far from being the norm, based on what I can observe in the voice market. Acceptability of vocal biomarkers by patients and the general public is not earned yet.” To generate public trust and acceptability, voice data privacy and protection must be at the core of development and strictly enforced regulation of voice-data processing is critical. Voice data are personal data that could be used to reveal a person's gender, age, language, geographical and socio-cultural origins, and health. Voice data might be particularly at risk given recent attempts using AI to deepfake voice clones for attempted cybercrime. As a result, Europol, the EU's law enforcement agency, urged member states to make “significant investments” in technologies that can detect deepfakes. Though there is some promise for the field and an abundance of investment and exuberance, there is still a long way to go before AI-powered vocal biomarkers can be endorsed by the clinical community. Better ethical and technical standards are required for this research to fully realise the potential for vocal biomarkers in the early detection of disease. COVID-19 detection from audio: seven grains of saltDigital mass testing for COVID-19 via a mobile phone application could be made possible through machine learning and its ability to identify patterns in data. COVID-19 appears to confer unique features in the audio produced by infected individuals,1 and machine learning COVID-19 detection from breath, cough, and speech audio recordings has yielded promising results.2–4 In this critique, we present seven major issues with this research and argue that further investigation is needed before conclusions about the detectability of COVID-19 from audio can be made. Full-Text PDF Open Access