AI-powered digital stethoscopes show promise in closing global TB screening gaps

As tuberculosis (TB) remains the world’s deadliest infectious disease, new research highlights the growing potential of artificial intelligence (AI)-enabled digital stethoscopes as a game-changing tool in global screening efforts—especially in underserved and hard-to-reach regions.

A commentary published in the journal Med (Cell Press) by an international team of researchers suggests that combining traditional stethoscopes with digital technology and AI could significantly strengthen TB screening systems. The experts argue that this approach may help overcome persistent challenges such as under-detection, high operational costs, and unequal access to diagnostic infrastructure.

The study, led by Madhukar Pai of McGill University in Canada, along with collaborators from the UAE, Germany, and Switzerland, notes that AI-enabled digital stethoscopes have already demonstrated encouraging accuracy in detecting lung and cardiovascular abnormalities. Early-stage TB studies have also produced promising results, indicating their potential role in large-scale screening programmes.

“AI-enabled digital stethoscopes have shown strong feasibility and accuracy in early research. However, training and validation in diverse, high-burden settings are essential to fully understand and expand the impact of this technology,” the researchers stated.

According to the World Health Organization (WHO), an estimated 2.7 million people with TB were missed by existing screening programmes. Traditional symptom-based screening methods often fail to identify individuals with asymptomatic or subclinical TB, allowing the disease to spread silently within communities.

Although the WHO has recently endorsed several AI-based computer-aided detection (CAD) tools and ultra-portable radiography systems for TB screening, the study highlights major limitations. High equipment costs, infrastructure requirements, and expensive operations make these solutions difficult to implement widely—particularly in low-resource primary healthcare settings. Additionally, the use of radiography poses challenges for vulnerable groups such as pregnant women due to radiation exposure concerns.

In contrast, AI-driven digital stethoscopes offer a more accessible alternative. Researchers point to AI’s growing ability to interpret acoustic biomarkers—subtle sound patterns linked to disease that are often inaudible to the human ear. These include cough sounds and lung auscultation signals, which can be analysed to detect abnormal respiratory patterns associated with TB.

Evidence from high-burden countries such as India, Peru, South Africa, Uganda, and Vietnam supports the potential of AI-enabled auscultation as an effective TB screening and triage tool. These studies suggest that sound-based diagnostics, powered by AI, could complement or even replace imaging-based approaches in certain contexts.

“AI digital stethoscopes may become practical alternatives to imaging-based TB screening methods, with the potential to democratise access to healthcare for populations underserved by radiography,” the researchers noted.

Beyond accessibility, the technology is also seen as scalable, low-cost, and person-centred—making it suitable for large-scale public health deployment. Experts believe this innovation could play a critical role in achieving global TB case-finding targets by enabling earlier detection and faster intervention.

As global health systems continue to search for efficient, affordable, and inclusive screening solutions, AI-powered digital stethoscopes are emerging as a promising frontier—offering a realistic pathway to bridge diagnostic gaps and strengthen the fight against tuberculosis worldwide.