By Devin Partida, technology writer and the editor-in-chief, ReHack.com.
Healthcare providers commonly listen to heart and lung sounds when examining people. They aim to pick up on abnormalities that give them more insights into patients’ conditions, and those diagnostic methods aren’t going anywhere.
However, pioneering research suggests that screening could also happen by analyzing someone’s voice with the help of artificial intelligence (AI). Here’s a look at some ongoing developments.
A Collaborative Effort Looks for Vocal Biomarkers
Even the most skilled physicians can’t always detect signs of trouble during a patient’s routine examination. That’s especially true if a person does not have external symptoms. However, biomarkers indicate possible abnormal processes within the body. Scientists have linked some of them to cancer and high cholesterol, for example.
Researchers at the Mayo Clinic recently teamed up with an Israeli company called Vocalis Health. The two organizations initially worked together to learn about voice-based biomarkers for pulmonary hypertension — an often undiagnosed condition that causes high blood pressure in the lungs. The earlier efforts established a connection between the disorder and specific vocal qualities. This recent undertaking seeks to identify the specific vocal biomarkers associated with the medical problem.
Vocalis Health’s technology works on any connected device, and it provides a noninvasive way to check for medical problems. Although this current initiative focuses on only one disorder, there are plans to expand the technology to apply to other issues.
Researchers Working on a Voice-Based COVID-19 Screening Tool
Predictive analytics tools have furthered impressive progress in the medical sector. However, they are not free from bias. Some medical technology companies strive to build algorithms that treat the data as objectively as possible. For example, one company combines three models to get a more holistic view of patient outcomes. It also identifies people across diverse populations to avoid overlooking underserved groups.