A cough is often dismissed as a minor inconvenience, but researchers in India, armed with cutting-edge AI technology from Google, are proving that it can reveal much more about our health than we might expect. This innovative approach, highlighted by Google Research, is leveraging artificial intelligence to detect serious respiratory diseases such as tuberculosis (TB) and chronic obstructive pulmonary disease (COPD) simply by analysing the sound of a cough.
This leap in medical technology is made possible by Google’s Health Acoustic Representations (HeAR), a bioacoustic foundation model launched in March. The HeAR model, which was trained on an immense dataset of 300 million pieces of audio, including approximately 100 million cough sounds, is designed to pick up on subtle patterns in health-related sounds that might otherwise go unnoticed.
The potential of this technology is not just theoretical. In practice, it’s already being used by Salcit Technologies, a respiratory healthcare company based in India, through their Swaasa app. The app employs HeAR to analyse cough sounds and detect tuberculosis, a disease that, while treatable, often goes undiagnosed due to limited access to healthcare services. This AI-powered solution could be a game-changer in the fight against TB, making diagnosis more accessible and affordable for millions of people worldwide.
Shravya Shetty, Google Research’s Director of Engineering, noted that HeAR has proven to be superior in its ability to capture meaningful patterns in health-related acoustic data. “We found that, on average, HeAR ranks higher than other models on a wide range of tasks and for generalising across microphones,” Shetty said. This means that the technology is not only effective but also versatile, capable of performing consistently across different audio recording devices.
The ability to analyse health through sound is not limited to detecting respiratory diseases. According to a research report, AI models can also assess how a person speaks—considering factors like tone, pitch, and pace—to detect potential health issues such as dementia. This kind of audio analysis offers a powerful foundation for broader medical applications, where AI could play a crucial role in early detection and diagnosis.
The implications of AI in healthcare extend far beyond respiratory diseases. For instance, in June, researchers at the University of Cambridge developed an AI model called EMethylNET, which shows promise in early cancer detection. This model can potentially determine the best course of treatment by analysing the genetic makeup of tumours, helping doctors make critical decisions during surgery. Dr. Gabriel Zada, a neurosurgery specialist at Keck Medicine of USC, highlighted the importance of this technology, stating that knowing the tumour type and subtype during surgery is becoming increasingly crucial.
Early cancer detection, facilitated by AI, is another area where technology is making significant strides. New York-based medtech company Ezra uses advanced imaging and AI to scan vital areas of the body, including the brain, lungs, liver, and prostate, for early signs of cancer. Dr. Daniel Sodickson, Ezra’s medical advisor, emphasised the life-saving potential of early detection, noting that survival rates for cancer can jump dramatically—from 20% to 80%—when the disease is caught early. This kind of early intervention could save billions of lives, demonstrating the profound impact AI can have on global health.
The use of AI in detecting and diagnosing diseases is a clear example of how technology is transforming medicine. By harnessing the power of machine learning, researchers can identify patterns in data that might be too subtle or complex for human analysis. This capability is particularly valuable in areas like respiratory health, where early diagnosis can significantly improve patient outcomes.
For tuberculosis, a disease that continues to affect millions of people worldwide, AI could provide a much-needed boost in detection efforts. TB is often referred to as a “silent killer” because its symptoms can be easily overlooked or mistaken for less serious conditions. The ability to diagnose TB through a simple cough analysis could lead to earlier detection, more timely treatment, and ultimately, fewer deaths from the disease.
Moreover, the implications of this technology go beyond individual diseases. The ability to analyse sound and detect a range of health issues could lead to the development of new diagnostic tools that are both non-invasive and widely accessible. This could be particularly valuable in regions where access to healthcare is limited, as it would allow for earlier diagnosis and treatment of diseases that might otherwise go undetected.
The integration of AI in healthcare is not without its challenges. Ensuring the accuracy and reliability of these models is critical, as is addressing concerns about data privacy and security. However, the potential benefits far outweigh the risks, particularly when it comes to improving access to healthcare and saving lives.
As AI continues to advance, we can expect to see even more innovative applications in the field of medicine. Whether it’s analysing coughs to detect TB or using genetic data to guide cancer treatment, AI is poised to revolutionise the way we diagnose and treat diseases. This technology represents a significant step forward in our ability to understand and address complex health issues, offering new hope to patients around the world.
In the end, the humble cough may turn out to be one of the most powerful diagnostic tools we have—thanks to the remarkable capabilities of AI.
