medical sound wave visualization in blue | feature | What is Acoustic Epidemiology? | The Future Of AI Diagnostics

The healthcare industry has always been a leader in innovation. With the help of machine learning (ML) and artificial intelligence (AI), it continues to advance and help people live healthier and longer lives. Acoustic epidemiology is evolving and providing data points on various medical disorders so that patient care can improve.

What Is Epidemiology? 

By definition, epidemiology aims to connect causes and risk factors of health-related events. These events are usually related to diseases, but other aspects of health can be studied as well. This field does its job mainly by analyzing systematically and carefully acquired data. Usually, epidemiological studies focus on specific populations, such as neighborhoods, cities, states, and countries.

What Is Acoustic Epidemiology? 

Acoustic Epidemiology consists in analyzing sounds (by themselves or in conjunction with other data points) as a clinical, objective epidemiological tool.

Devices include more powerful computing hardware and more accurate sensors than ever before. This progress means there is an increased potential for ML and AI to gain more sensitivity. Ultimately, these devices can capture and process more sounds and compare them to each other or with preexisting datasets. For instance, these could include sneezing, snoring, breathing, other sounds, and other data collected from simple sensors.

What Role Does AI Have in Acoustic Epidemiology?

On the one hand, the public wants reliable healthcare information. And on the other hand, healthcare workers’ time is precious. So, the healthcare industry is using AI to bridge the gap.

Medical facilities are using chatbots to assist patients and educate them during the crisis.

AI-powered chatbots can ask questions about demographics to help trace infections, as well as recoveries.

In acoustic epidemiology and syndromic surveillance, having the ability to track sounds could help to identify cough variances and anomalies faster than traditional reporting. Being able to identify a health threat earlier can lead to more successful public health actions.

From an epidemiological perspective, a single cough, from one person, means very little. After all, healthy people cough roughly a dozen times a day. Since cough is so common, even among the healthy, it doesn’t usually raise eyebrows. In short, cough is common and commonly ignored.

What Are the Advantages of Using AI in Diagnostics?

Both patients and healthcare providers can benefit from AI performing tasks that remove the need for a medical specialist. Reliability is probably the main factor. For instance, the delivery of consistent results can enhance performance and cost-efficiency in a healthcare setting. This automation would mean practitioners could become more accessible for patients. In other words, practitioners can see more patients and diagnose them earlier. Above all, this could reduce severe cases and improve treatment.

What Could the Future of AI Diagnostics Look Like?

Studies now show that AI software is outperforming humans in important diagnostic tasks, which increases the prospects of a doctorless diagnosis at some point in the future.

Man and machine may collaborate for some time. Still, the med-tech industry is opening up to smart software. Furthermore, funds are pouring into research efforts to develop and evolve learning algorithms. AI has already replaced humans on some tasks, but where the limits lie is debatable.

As AI can sift through vast amounts of data, it certainly has the potential to take the burden away from overworked doctors, nurses, etc.

What Are Other Acoustic Diagnostics Applications There?

Acoustic diagnostics with AI and ML are outside the medical industry as well. For instance, in manufacturing, increasingly sophisticated products and processes benefit from acoustic diagnostics. Namely, acoustic diagnostics facilitate maintenance and quality control. In this context, devices equipped with sensors and AI monitor and assess noise patterns in manufacturing plant machinery. Hence, these systems can detect problems acoustically.

Have you tried health tracking apps? What was your experience? Share your thoughts in the comment section below!

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